Adaptive signal processing for infusion devices and related methods and systems

ABSTRACT

Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device involves obtaining a filtered measurement indicative of a physiological condition of a user, determining a metric indicative of a characteristic of the filtered measurement based at least in part on one or more derivative metrics associated with the filtered measurement, and determining an output measurement indicative of the physiological condition of the user based at least in part on the filtered measurement, the metric, and a previous output measurement.

CROSS-REFERENCE TO RELATED APPLICATIONS

The subject matter described herein is also related to the subjectmatter described in U.S. patent application Ser. No. ______ (AttorneyDocket No. 009.5088 (C00007566.USU1)) and U.S. patent application Ser.No. ______ (Attorney Docket No. 009.5089 (C00007567.USU1)), both filedconcurrently herewith and incorporated by reference in their entirety.

TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally tomedical devices, and more particularly, embodiments of the subjectmatter relate to controlling operations of a portable electronic device,such as a fluid infusion device.

BACKGROUND

Infusion pump devices and systems are relatively well known in themedical arts, for use in delivering or dispensing an agent, such asinsulin or another prescribed medication, to a patient. A typicalinfusion pump includes a pump drive system which typically includes asmall motor and drive train components that convert rotational motormotion to a translational displacement of a plunger (or stopper) in areservoir that delivers medication from the reservoir to the body of auser via a fluid path created between the reservoir and the body of auser. Use of infusion pump therapy has been increasing, especially fordelivering insulin for diabetics.

Continuous insulin infusion provides greater control of a diabetic'scondition, and hence, control schemes are being developed that allowinsulin infusion pumps to monitor and regulate a user's blood glucoselevel in a substantially continuous and autonomous manner, for example,overnight while the user is sleeping. Rather than continuously samplingand monitoring a user's blood glucose level, which may compromisebattery life, intermittently sensed glucose data samples may be utilizedfor determining operating commands for the infusion pump. To achieve thedesired level of accuracy and reliability and reduce the impact of noiseand other spurious signals, the sensor data is filtered and calibratedusing a known good blood glucose value (e.g., a fingerstickmeasurement). However, the filtering introduces the appearance of lag,which can degrade the user experience. Additionally, various factors canlead to transient changes in the sensor output, which may influence theaccuracy of the calibration. Degradation of sensor performance over timemay further compound these problems. Accordingly, it is desirable toimprove accuracy and reliability while also reducing lag and improvingthe overall user experience.

BRIEF SUMMARY

An embodiment of a method is provided for operating an infusion deviceoperable to deliver fluid capable of influencing a physiologicalcondition to a user. An exemplary method involves obtaining a filteredmeasurement indicative of the physiological condition of the user,determining a metric indicative of a characteristic of the filteredmeasurement based at least in part on one or more derivative metricsassociated with the filtered measurement, and determining an outputmeasurement indicative of the physiological condition of the user basedat least in part on the filtered measurement, the metric, and a previousoutput measurement.

In one embodiment, an apparatus for a device is provided. The devicecomprises a sensing element to provide one or more signals influenced bya physiological condition of a user and a control module coupled to thesensing element. The control module determines a filtered measurementbased on the one or more signals, determines a metric indicative of acharacteristic of the filtered measurement based at least in part on oneor more derivative metrics associated with the filtered measurement, anddetermines an output measurement indicative of the physiologicalcondition of the user based at least in part on the filteredmeasurement, the metric, and a previous output measurement.

In another embodiment, an infusion system is provided. The infusionsystem comprises an infusion device and a sensing arrangement includinga sensing element to provide one or more signals corresponding to aphysiological condition in a body of a user that is sensed by thesensing element. The sensing arrangement determines an outputmeasurement indicative of the physiological condition based at least inpart on a filtered measurement based on the one or more signals, ametric indicative of a characteristic of the filtered measurement, and aprevious output measurement. The infusion device comprises a motoroperable to deliver fluid influencing the physiological condition to thebody of the user and a control system to receive the output measurementand determine a delivery command for operating the motor based at leastin part on the output measurement.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures, which may beillustrated for simplicity and clarity and are not necessarily drawn toscale.

FIG. 1 depicts an exemplary embodiment of an infusion system;

FIG. 2 depicts a plan view of an exemplary embodiment of a fluidinfusion device suitable for use in the infusion system of FIG. 1;

FIG. 3 is an exploded perspective view of the fluid infusion device ofFIG. 2;

FIG. 4 is a cross-sectional view of the fluid infusion device of FIGS.2-3 as viewed along line 4-4 in FIG. 3 when assembled with a reservoirinserted in the infusion device;

FIG. 5 is a block diagram of an exemplary control system suitable foruse in a fluid infusion device, such as the fluid infusion device ofFIG. 1;

FIG. 6 is a block diagram of an exemplary electronic device suitable foruse as the sensing arrangement in the control system of FIG. 5;

FIG. 7 is a block diagram of an exemplary signal processing systemsuitable for implementation by the data management application in thesensing arrangement of FIG. 6;

FIG. 8 is a flow diagram of an exemplary artifact detection processsuitable for implementation by the artifact detection module in thesignal processing system of FIG. 7;

FIG. 9 is a flow diagram of an exemplary signal analysis processsuitable for implementation by the signal analysis module in the signalprocessing system of FIG. 7;

FIG. 10 is a flow diagram of an exemplary dropout detection processsuitable for implementation by the dropout detection module in thesignal processing system of FIG. 7;

FIG. 11 is a flow diagram of an exemplary adaptive filtering processsuitable for implementation by the adaptive filtering module in thesignal processing system of FIG. 7;

FIG. 12 is a flow diagram of an exemplary calibration process suitablefor implementation by the calibration application in the sensingarrangement of FIG. 6;

FIG. 13 is a flow diagram of an exemplary calibration factordetermination process suitable for implementation in conjunction withthe calibration process of FIG. 12;

FIG. 14 is a flow diagram of an exemplary dynamic adjustment processsuitable for implementation in conjunction with the calibration factordetermination process of FIG. 13; and

FIG. 15 is a flow diagram of an exemplary sensor monitoring processsuitable for implementation by the health monitoring application in thesensing arrangement of FIG. 6.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description.

While the subject matter described herein may be implemented in anyelectronic device, exemplary embodiments described below are implementedin the form of medical devices, such as portable electronic medicaldevices. Although many different applications are possible, thefollowing description focuses on a fluid infusion device (or infusionpump) as part of an infusion system deployment. For the sake of brevity,conventional techniques related to infusion system operation, insulinpump and/or infusion set operation, and other functional aspects of thesystems (and the individual operating components of the systems) may notbe described in detail here. Examples of infusion pumps may be of thetype described in, but not limited to, U.S. Pat. Nos. 4,562,751;4,685,903; 5,080,653; 5,505,709; 5,097,122; 6,485,465; 6,554,798;6,558,320; 6,558,351; 6,641,533; 6,659,980; 6,752,787; 6,817,990;6,932,584; and 7,621,893; each of which are herein incorporated byreference.

Embodiments of the subject matter described herein generally relate tofluid infusion devices including a motor that is operable to linearlydisplace a plunger (or stopper) of a reservoir provided within the fluidinfusion device to deliver a dosage of fluid, such as insulin, to thebody of a user. As described in greater detail below, in exemplaryembodiments, the dosage commands that govern operation of the motor areinfluenced by a current (or most recent) measurement of a condition inthe body of the user. For example, in one or more embodiments, aninsulin dosage (or delivery) command may be determined based on adifference between a current glucose measurement obtained for the userand a target glucose value for the user, where the current glucosemeasurement is determined based at least in part on one or more outputsignals obtained from a sensing element configured to sense, measure, orotherwise detect the relative amount of glucose present in the user'sbody. While the subject matter may be described herein in the context ofdelivering insulin to regulate a glucose level in the body of a user forpurposes of explanation, it will be appreciated that the subject matterdescribed herein it not limited to any particular type of fluid beingdelivered or any particular physiological condition being regulated.

As described in greater detail below in the context of FIGS. 6-11, inexemplary embodiments, the output signals from a sensing elementsensitive to a user's glucose level are filtered to obtain filteredmeasurements indicative of the user's glucose level. The filteredmeasurements are analyzed to determine metrics indicative of signalcharacteristics of the filtered measurement signal based at least inpart on one or more derivative metrics associated with the filteredmeasurements. For example, a first derivative metric associated with arespective filtered measurement may be calculated and utilized todetermine a frequency metric indicative of an estimated frequency of thefiltered measurement signal, while a second derivative metric may becalculated and utilized to determine a noise metric indicative of anestimate of the amount of noise present in the filtered measurementsignal. A respective filtered measurement is adaptively filtered in amanner that is influenced by its associated frequency and noise metricsto obtain an uncalibrated measurement indicative of the user's glucoselevel that may be output or otherwise provided to another component ofan infusion system, which, in turn, converts the uncalibrated outputmeasurement into a calibrated sensor glucose value (or sensed glucosevalue) using a calibration factor associated with the sensing element.The sensed glucose value may be utilized in determining a deliverycommand, which, in turn, is utilized to operate a motor of the infusiondevice to deliver insulin to the body of the user.

In one or more embodiments, the calibration factor used to convert theuncalibrated output measurement determined by filtering output signalsfrom the sensing element into a calibrated sensor glucose value isadjusted in a manner that is influenced by an expected calibrationfactor, as described in greater detail below in the context of FIGS.12-14. A raw (or unadjusted) calibration factor may be determined basedon the relationship between reference blood glucose measurements andcorresponding uncalibrated output measurements that are paired with therespective reference blood glucose measurements. For example, in oneembodiment, the raw calibration factor is determined as a ratio of afirst weighted sum to a second weighted sum, where the first weightedsum is a sum of the products of the first weighted sum comprises a sumof products of the respective uncalibrated measurement and referencemeasurement of each respective pairing and one or more weighting factorsassociated with that respective pairing, while the second weighted sumcomprises a sum of products of a square of the respective uncalibratedmeasurement and one or more weighting factors associated with itsrespective pairing. Thereafter, an expected calibration factor for thesensing element is utilized to determine an adjusted calibration factorbased on the raw calibration factor. In this manner, the value of theadjusted calibration factor is adjusted from the value of the rawcalibration factor towards the value of the expected calibration factor.For example, in one embodiment, the adjusted calibration factor iscalculated as a weighted sum of the raw calibration factor and theexpected calibration factor. In other embodiments, the adjustedcalibration factor may be determined as a function of the rawcalibration factor and an elapsed time since the most recent calibrationattempt, where the function is configured to dynamically adjust thevalue of the adjusted calibration factor from the value of the rawcalibration factor towards the expected calibration factor as theelapsed time increases.

As described in greater detail below in the context of FIG. 15, inexemplary embodiments, the filtered measurements based on the outputsignals from the sensing element are analyzed over a monitoring periodto determine signal characteristics associated with the filteredmeasurement signal over the monitoring period. The signalcharacteristics associated with the filtered measurement signal over themonitoring period are, in turn, utilized to determine a reliabilitymetric associated with the sensing element for the monitoring period. Inexemplary embodiments, the reliability metric is indicative of therelationship between the noisiness of the filtered measurement signaland the magnitude of the filtered measurement signal. For example, inone embodiment, the reliability metric corresponds to a ratio of theaverage value of the filtered measurement signal over the monitoringperiod to a number of high noise measurements that occurred during thatmonitoring period. In this regard, the respective values of the subsetof filtered measurements occurring during the monitoring period areaveraged to obtain the average measurement value for the monitoringperiod. To determine the number of high noise measurements, a noisemetric for each respective measurement of the subset of filteredmeasurements occurring during the monitoring period may be compared to anoise threshold to classify or otherwise identify that respectivemeasurement as a high noise measurement when the value of noise metricexceeds the noise threshold value. Thereafter, the average measurementvalue for the monitoring period is divided by the number of high noisemeasurements to obtain a reliability metric indicative of therelationship between the magnitude and the noisiness of the filteredmeasurement signal.

When the reliability metric violates a maintenance threshold value, anindication of a maintenance condition may be generated or otherwiseprovided to a user. For example, an auditory and/or visual usernotification may be automatically generated that indicates replacementof the sensing element should be performed, or alternatively, that someother maintenance of the sensing element should otherwise be performed(e.g., inspecting electrical connectivity to/from the sensing element,ensuring the sensing element is properly inserted and/or fitted in ahousing of a sensing device, or the like). In one or more embodiments,the indication of a maintenance condition is automatically provided onlywhen the reliability metric is less than the maintenance threshold valuefor at least a threshold number of consecutive monitoring periods. Forexample, in one embodiment, the monitoring periods have a duration oftwo hours, where a user notification is automatically generated when thereliability metric is less than the maintenance threshold value for atleast three (or more than two) consecutive monitoring periods, or inother words, when the output from the sensing element indicatesrelatively low reliability over the course of at least six hours. Thus,in such embodiments, the reliability metric may fall below themaintenance threshold for a monitoring period (e.g., as a result ofinterference or some other transient condition) before recovering tovalues above maintenance threshold for subsequent monitoring periodswithout any user notifications being generated. As a result, the usablelifetime of the sensing element may be extended by avoiding prematurelyindicating replacement.

Turning now to FIG. 1, one exemplary embodiment of an infusion system100 includes, without limitation, a fluid infusion device (or infusionpump) 102, a sensing arrangement 104, a command control device (CCD)106, and a computing device 108. The components of an infusion system100 may be realized using different platforms, designs, andconfigurations, and the embodiment shown in FIG. 1 is not exhaustive orlimiting. In practice, the infusion device 102 and the sensingarrangement 104 are secured at desired locations on the body of a user(or patient), as illustrated in FIG. 1. In this regard, the locations atwhich the infusion device 102 and the sensing arrangement 104 aresecured to the body of the user in FIG. 1 are provided only as arepresentative, non-limiting, example. The elements of the infusionsystem 100 may be similar to those described in U.S. Pat. No. 8,674,288,the subject matter of which is hereby incorporated by reference in itsentirety.

In the illustrated embodiment of FIG. 1, the infusion device 102 isdesigned as a portable medical device suitable for infusing a fluid, aliquid, a gel, or other agent into the body of a user. In exemplaryembodiments, the infused fluid is insulin, although many other fluidsmay be administered through infusion such as, but not limited to, HIVdrugs, drugs to treat pulmonary hypertension, iron chelation drugs, painmedications, anti-cancer treatments, medications, vitamins, hormones, orthe like. In some embodiments, the fluid may include a nutritionalsupplement, a dye, a tracing medium, a saline medium, a hydrationmedium, or the like.

The sensing arrangement 104 generally represents the components of theinfusion system 100 configured to sense, detect, measure or otherwisequantify a condition of the user, and may include a sensor, a monitor,or the like, for providing data indicative of the condition that issensed, detected, measured or otherwise monitored by the sensingarrangement. In this regard, the sensing arrangement 104 may includeelectronics and enzymes reactive to a biological condition, such as ablood glucose level, or the like, of the user, and provide dataindicative of the blood glucose level to the infusion device 102, theCCD 106 and/or the computing device 108. For example, the infusiondevice 102, the CCD 106 and/or the computing device 108 may include adisplay for presenting information or data to the user based on thesensor data received from the sensing arrangement 104, such as, forexample, a current glucose level of the user, a graph or chart of theuser's glucose level versus time, device status indicators, alertmessages, or the like. In other embodiments, the infusion device 102,the CCD 106 and/or the computing device 108 may include electronics andsoftware that are configured to analyze sensor data and operate theinfusion device 102 to deliver fluid to the body of the user based onthe sensor data and/or preprogrammed delivery routines. Thus, inexemplary embodiments, one or more of the infusion device 102, thesensing arrangement 104, the CCD 106, and/or the computing device 108includes a transmitter, a receiver, and/or other transceiver electronicsthat allow for communication with other components of the infusionsystem 100, so that the sensing arrangement 104 may transmit sensor dataor monitor data to one or more of the infusion device 102, the CCD 106and/or the computing device 108.

Still referring to FIG. 1, in various embodiments, the sensingarrangement 104 may be secured to the body of the user or embedded inthe body of the user at a location that is remote from the location atwhich the infusion device 102 is secured to the body of the user. Invarious other embodiments, the sensing arrangement 104 may beincorporated within the infusion device 102. In other embodiments, thesensing arrangement 104 may be separate and apart from the infusiondevice 102, and may be, for example, part of the CCD 106. In suchembodiments, the sensing arrangement 104 may be configured to receive abiological sample, analyte, or the like, to measure a condition of theuser.

As described above, in some embodiments, the CCD 106 and/or thecomputing device 108 may include electronics and other componentsconfigured to perform processing, delivery routine storage, and tocontrol the infusion device 102 in a manner that is influenced by sensordata measured by and/or received from the sensing arrangement 104. Byincluding control functions in the CCD 106 and/or the computing device108, the infusion device 102 may be made with more simplifiedelectronics. However, in other embodiments, the infusion device 102 mayinclude all control functions, and may operate without the CCD 106and/or the computing device 108. In addition, the infusion device 102and/or the sensing arrangement 104 may be configured to transmit data tothe CCD 106 and/or the computing device 108 for display or processing ofthe data by the CCD 106 and/or the computing device 108. In variousembodiments, the CCD 106 and/or the computing device 108 may be aportable electronic device, such as a mobile phone, a smartphone, atablet computer, a notebook or laptop computer, or the like. In oneembodiment, the computing device 108 is a server computer that isaccessible via a communications network, such as the Internet, acellular network, or the like.

In some embodiments, the CCD 106 and/or the computing device 108 mayprovide information to the user that facilitates the user's subsequentuse of the infusion device 102. For example, the CCD 106 may provideinformation to the user to allow the user to determine the rate or doseof medication to be administered into the user's body. In otherembodiments, the CCD 106 may provide information to the infusion device102 to autonomously control the rate or dose of medication administeredinto the body of the user. In some embodiments, the sensing arrangement104 may be integrated into the CCD 106. Such embodiments may allow theuser to monitor a condition by providing, for example, a sample of hisor her blood to the sensing arrangement 104 to assess his or hercondition. In some embodiments, the sensing arrangement 104 and the CCD106 may be used for determining glucose levels in the blood and/or bodyfluids of the user without the use of, or necessity of, a wire or cableconnection between the infusion device 102 and the sensing arrangement104 and/or the CCD 106.

In some embodiments, the sensing arrangement 104 and/or the infusiondevice 102 are cooperatively configured to utilize a closed-loop systemfor delivering fluid to the user. Examples of sensing devices and/orinfusion pumps utilizing closed-loop systems may be found at, but arenot limited to, the following U.S. Pat. Nos. 6,088,608, 6,119,028,6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402, 153, all of whichare incorporated herein by reference in their entirety. In suchembodiments, the sensing arrangement 104 is configured to sense ormeasure a condition of the user, such as, blood glucose level or thelike. The infusion device 102 is configured to deliver fluid in responseto the condition sensed by the sensing arrangement 104. In turn, thesensing arrangement 104 continues to sense or otherwise quantify acurrent condition of the user, thereby allowing the infusion device 102to deliver fluid continuously in response to the condition currently (ormost recently) sensed by the sensing arrangement 104 indefinitely. Insome embodiments, the sensing arrangement 104 and/or the infusion device102 may be configured to utilize the closed-loop system only for aportion of the day, for example only when the user is asleep or awake.

FIGS. 2-4 depict one exemplary embodiment of a fluid infusion device 200(or alternatively, infusion pump) suitable for use in an infusionsystem, such as, for example, as infusion device 102 in the infusionsystem 100 of FIG. 1. The fluid infusion device 200 is a portablemedical device designed to be carried or worn by a patient (or user),and the fluid infusion device 200 may leverage any number ofconventional features, components, elements, and characteristics ofexisting fluid infusion devices, such as, for example, some of thefeatures, components, elements, and/or characteristics described in U.S.Pat. Nos. 6,485,465 and 7,621,893. It should be appreciated that FIGS.2-4 depict some aspects of the infusion device 200 in a simplifiedmanner; in practice, the infusion device 200 could include additionalelements, features, or components that are not shown or described indetail herein.

As best illustrated in FIGS. 2-3, the illustrated embodiment of thefluid infusion device 200 includes a housing 202 adapted to receive afluid-containing reservoir 205. An opening 220 in the housing 202accommodates a fitting 223 (or cap) for the reservoir 205, with thefitting 223 being configured to mate or otherwise interface with tubing221 of an infusion set 225 that provides a fluid path to/from the bodyof the user. In this manner, fluid communication from the interior ofthe reservoir 205 to the user is established via the tubing 221. Theillustrated fluid infusion device 200 includes a human-machine interface(HMI) 230 (or user interface) that includes elements 232, 234 that canbe manipulated by the user to administer a bolus of fluid (e.g.,insulin), to change therapy settings, to change user preferences, toselect display features, and the like. The infusion device also includesa display element 226, such as a liquid crystal display (LCD) or anothersuitable display element, that can be used to present various types ofinformation or data to the user, such as, without limitation: thecurrent glucose level of the patient; the time; a graph or chart of thepatient's glucose level versus time; device status indicators; etc.

The housing 202 is formed from a substantially rigid material having ahollow interior 214 adapted to allow an electronics assembly 204, asliding member (or slide) 206, a drive system 208, a sensor assembly210, and a drive system capping member 212 to be disposed therein inaddition to the reservoir 205, with the contents of the housing 202being enclosed by a housing capping member 216. The opening 220, theslide 206, and the drive system 208 are coaxially aligned in an axialdirection (indicated by arrow 218), whereby the drive system 208facilitates linear displacement of the slide 206 in the axial direction218 to dispense fluid from the reservoir 205 (after the reservoir 205has been inserted into opening 220), with the sensor assembly 210 beingconfigured to measure axial forces (e.g., forces aligned with the axialdirection 218) exerted on the sensor assembly 210 responsive tooperating the drive system 208 to displace the slide 206. In variousembodiments, the sensor assembly 210 may be utilized to detect one ormore of the following: an occlusion in a fluid path that slows,prevents, or otherwise degrades fluid delivery from the reservoir 205 toa user's body; when the reservoir 205 is empty; when the slide 206 isproperly seated with the reservoir 205; when a fluid dose has beendelivered; when the infusion pump 200 is subjected to shock orvibration; when the infusion pump 200 requires maintenance.

Depending on the embodiment, the fluid-containing reservoir 205 may berealized as a syringe, a vial, a cartridge, a bag, or the like. Incertain embodiments, the infused fluid is insulin, although many otherfluids may be administered through infusion such as, but not limited to,HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs,pain medications, anti-cancer treatments, medications, vitamins,hormones, or the like. As best illustrated in FIGS. 3-4, the reservoir205 typically includes a reservoir barrel 219 that contains the fluidand is concentrically and/or coaxially aligned with the slide 206 (e.g.,in the axial direction 218) when the reservoir 205 is inserted into theinfusion pump 200. The end of the reservoir 205 proximate the opening220 may include or otherwise mate with the fitting 223, which securesthe reservoir 205 in the housing 202 and prevents displacement of thereservoir 205 in the axial direction 218 with respect to the housing 202after the reservoir 205 is inserted into the housing 202. As describedabove, the fitting 223 extends from (or through) the opening 220 of thehousing 202 and mates with tubing 221 to establish fluid communicationfrom the interior of the reservoir 205 (e.g., reservoir barrel 219) tothe user via the tubing 221 and infusion set 225. The opposing end ofthe reservoir 205 proximate the slide 206 includes a plunger 217 (orstopper) positioned to push fluid from inside the barrel 219 of thereservoir 205 along a fluid path through tubing 221 to a user. The slide206 is configured to mechanically couple or otherwise engage with theplunger 217, thereby becoming seated with the plunger 217 and/orreservoir 205. Fluid is forced from the reservoir 205 via tubing 221 asthe drive system 208 is operated to displace the slide 206 in the axialdirection 218 toward the opening 220 in the housing 202.

In the illustrated embodiment of FIGS. 3-4, the drive system 208includes a motor assembly 207 and a drive screw 209. The motor assembly207 includes a motor that is coupled to drive train components of thedrive system 208 that are configured to convert rotational motor motionto a translational displacement of the slide 206 in the axial direction218, and thereby engaging and displacing the plunger 217 of thereservoir 205 in the axial direction 218. In some embodiments, the motorassembly 207 may also be powered to translate the slide 206 in theopposing direction (e.g., the direction opposite direction 218) toretract and/or detach from the reservoir 205 to allow the reservoir 205to be replaced. In exemplary embodiments, the motor assembly 207includes a brushless DC (BLDC) motor having one or more permanentmagnets mounted, affixed, or otherwise disposed on its rotor. However,the subject matter described herein is not necessarily limited to usewith BLDC motors, and in alternative embodiments, the motor may berealized as a solenoid motor, an AC motor, a stepper motor, apiezoelectric caterpillar drive, a shape memory actuator drive, anelectrochemical gas cell, a thermally driven gas cell, a bimetallicactuator, or the like. The drive train components may comprise one ormore lead screws, cams, ratchets, jacks, pulleys, pawls, clamps, gears,nuts, slides, bearings, levers, beams, stoppers, plungers, sliders,brackets, guides, bearings, supports, bellows, caps, diaphragms, bags,heaters, or the like. In this regard, although the illustratedembodiment of the infusion pump utilizes a coaxially aligned drivetrain, the motor could be arranged in an offset or otherwise non-coaxialmanner, relative to the longitudinal axis of the reservoir 205.

As best shown in FIG. 4, the drive screw 209 mates with threads 402internal to the slide 206. When the motor assembly 207 is powered andoperated, the drive screw 209 rotates, and the slide 206 is forced totranslate in the axial direction 218. In an exemplary embodiment, theinfusion pump 200 includes a sleeve 211 to prevent the slide 206 fromrotating when the drive screw 209 of the drive system 208 rotates. Thus,rotation of the drive screw 209 causes the slide 206 to extend orretract relative to the drive motor assembly 207. When the fluidinfusion device is assembled and operational, the slide 206 contacts theplunger 217 to engage the reservoir 205 and control delivery of fluidfrom the infusion pump 200. In an exemplary embodiment, the shoulderportion 215 of the slide 206 contacts or otherwise engages the plunger217 to displace the plunger 217 in the axial direction 218. Inalternative embodiments, the slide 206 may include a threaded tip 213capable of being detachably engaged with internal threads 404 on theplunger 217 of the reservoir 205, as described in detail in U.S. Pat.Nos. 6,248,093 and 6,485,465, which are incorporated by referenceherein.

As illustrated in FIG. 3, the electronics assembly 204 includes controlelectronics 224 coupled to the display element 226, with the housing 202including a transparent window portion 228 that is aligned with thedisplay element 226 to allow the display 226 to be viewed by the userwhen the electronics assembly 204 is disposed within the interior 214 ofthe housing 202. The control electronics 224 generally represent thehardware, firmware, processing logic and/or software (or combinationsthereof) configured to control operation of the motor assembly 207and/or drive system 208, as described in greater detail below in thecontext of FIG. 5. Whether such functionality is implemented ashardware, firmware, a state machine, or software depends upon theparticular application and design constraints imposed on the embodiment.Those familiar with the concepts described here may implement suchfunctionality in a suitable manner for each particular application, butsuch implementation decisions should not be interpreted as beingrestrictive or limiting. In an exemplary embodiment, the controlelectronics 224 includes one or more programmable controllers that maybe programmed to control operation of the infusion pump 200.

The motor assembly 207 includes one or more electrical leads 236 adaptedto be electrically coupled to the electronics assembly 204 to establishcommunication between the control electronics 224 and the motor assembly207. In response to command signals from the control electronics 224that operate a motor driver (e.g., a power converter) to regulate theamount of power supplied to the motor from a power supply, the motoractuates the drive train components of the drive system 208 to displacethe slide 206 in the axial direction 218 to force fluid from thereservoir 205 along a fluid path (including tubing 221 and an infusionset), thereby administering doses of the fluid contained in thereservoir 205 into the user's body. Preferably, the power supply isrealized one or more batteries contained within the housing 202.Alternatively, the power supply may be a solar panel, capacitor, AC orDC power supplied through a power cord, or the like. In someembodiments, the control electronics 224 may operate the motor of themotor assembly 207 and/or drive system 208 in a stepwise manner,typically on an intermittent basis; to administer discrete precise dosesof the fluid to the user according to programmed delivery profiles.

Referring to FIGS. 2-4, as described above, the user interface 230includes HMI elements, such as buttons 232 and a directional pad 234,that are formed on a graphic keypad overlay 231 that overlies a keypadassembly 233, which includes features corresponding to the buttons 232,directional pad 234 or other user interface items indicated by thegraphic keypad overlay 231. When assembled, the keypad assembly 233 iscoupled to the control electronics 224, thereby allowing the HMIelements 232, 234 to be manipulated by the user to interact with thecontrol electronics 224 and control operation of the infusion pump 200,for example, to administer a bolus of insulin, to change therapysettings, to change user preferences, to select display features, to setor disable alarms and reminders, and the like. In this regard, thecontrol electronics 224 maintains and/or provides information to thedisplay 226 regarding program parameters, delivery profiles, pumpoperation, alarms, warnings, statuses, or the like, which may beadjusted using the HMI elements 232, 234. In various embodiments, theHMI elements 232, 234 may be realized as physical objects (e.g.,buttons, knobs, joysticks, and the like) or virtual objects (e.g., usingtouch-sensing and/or proximity-sensing technologies). For example, insome embodiments, the display 226 may be realized as a touch screen ortouch-sensitive display, and in such embodiments, the features and/orfunctionality of the HMI elements 232, 234 may be integrated into thedisplay 226 and the HMI 230 may not be present. In some embodiments, theelectronics assembly 204 may also include alert generating elementscoupled to the control electronics 224 and suitably configured togenerate one or more types of feedback, such as, without limitation:audible feedback; visual feedback; haptic (physical) feedback; or thelike.

Referring to FIGS. 3-4, in accordance with one or more embodiments, thesensor assembly 210 includes a back plate structure 250 and a loadingelement 260. The loading element 260 is disposed between the cappingmember 212 and a beam structure 270 that includes one or more beamshaving sensing elements disposed thereon that are influenced bycompressive force applied to the sensor assembly 210 that deflects theone or more beams, as described in greater detail in U.S. patentapplication Ser. No. 12/908,807, which is incorporated by referenceherein. In exemplary embodiments, the back plate structure 250 isaffixed, adhered, mounted, or otherwise mechanically coupled to thebottom surface 238 of the drive system 208 such that the back platestructure 250 resides between the bottom surface 238 of the drive system208 and the housing cap 216. The drive system capping member 212 iscontoured to accommodate and conform to the bottom of the sensorassembly 210 and the drive system 208. The drive system capping member212 may be affixed to the interior of the housing 202 to preventdisplacement of the sensor assembly 210 in the direction opposite thedirection of force provided by the drive system 208 (e.g., the directionopposite direction 218). Thus, the sensor assembly 210 is positionedbetween the motor assembly 207 and secured by the capping member 212,which prevents displacement of the sensor assembly 210 in a downwarddirection opposite the direction of arrow 218, such that the sensorassembly 210 is subjected to a reactionary compressive force when thedrive system 208 and/or motor assembly 207 is operated to displace theslide 206 in the axial direction 218 in opposition to the fluid pressurein the reservoir 205. Under normal operating conditions, the compressiveforce applied to the sensor assembly 210 is correlated with the fluidpressure in the reservoir 205. As shown, electrical leads 240 areadapted to electrically couple the sensing elements of the sensorassembly 210 to the electronics assembly 204 to establish communicationto the control electronics 224, wherein the control electronics 224 areconfigured to measure, receive, or otherwise obtain electrical signalsfrom the sensing elements of the sensor assembly 210 that are indicativeof the force applied by the drive system 208 in the axial direction 218.

FIG. 5 depicts an exemplary embodiment of a control system 500 suitablefor use with an infusion device 502, such as the infusion device 102 inFIG. 1 or the infusion device 200 of FIG. 2. The control system 500 isconfigured to control or otherwise regulate a physiological condition inthe body 501 of a user to a desired (or target) value or otherwisemaintain the condition within a range of acceptable values. In one ormore exemplary embodiments, the condition being regulated is sensed,detected, measured or otherwise quantified by a sensing arrangement 504(e.g., sensing arrangement 104) communicatively coupled to the infusiondevice 502. However, it should be noted that in alternative embodiments,the condition being regulated by the control system 500 may becorrelative to the measured values obtained by the sensing arrangement504. That said, for clarity and purposes of explanation, the subjectmatter may be described herein in the context of the sensing arrangement504 being realized as a glucose sensing arrangement that senses,detects, measures or otherwise quantifies the user's glucose level,which is being regulated in the body 501 of the user by the controlsystem 500.

As described in greater detail below in the context of FIG. 6, inexemplary embodiments, the sensing arrangement 504 includes one or moreinterstitial glucose sensing elements generate or otherwise outputelectrical signals having a signal characteristic that is correlativeto, influenced by, or otherwise indicative of the relative interstitialfluid glucose level in the body 501 of the user. The output electricalsignals are filtered or otherwise processed to obtain a filteredmeasurement value indicative of the user's interstitial fluid glucoselevel. In exemplary embodiments, the control system 500 includes a bloodglucose meter 530, such as a finger stick device, which is configured todirectly sense, detect, measure or otherwise quantify the blood glucosein the body 501 of the user. In this regard, the blood glucose meter 530outputs or otherwise provides a measured blood glucose value that may beutilized as a reference measurement for calibrating the sensingarrangement 504 and converting an uncalibrated filtered measurementvalue indicative of the user's interstitial fluid glucose level into acorresponding calibrated blood glucose value, as described in greaterdetail below. For purposes of explanation, the calibrated blood glucosevalue calculated based on the electrical signals output by the sensingelement(s) of the sensing arrangement 504 may alternatively be referredto herein as the sensor glucose value, the sensed glucose value, orvariants thereof.

In the illustrated embodiment, the pump control system 520 generallyrepresents the electronics and other components of the infusion device502 that control operation of the fluid infusion device 502 according toa desired infusion delivery program in a manner that is influenced bythe sensed glucose value indicative of the current blood glucose levelin the body 501 of the user. To support closed-loop control, the pumpcontrol system 520 maintains, receives, or otherwise obtains a desiredvalue for a condition in the body 501 of the user to be regulated (e.g.,a target or commanded glucose value), and generates or otherwisedetermines dosage commands for operating the motor 507 to displace theplunger 517 based at least in part on a difference between the sensedglucose value and the target glucose value. In practice, the infusiondevice 502 may store or otherwise maintain the target value in a datastorage element accessible to the pump control system 520. The targetvalue may be received from an external component (e.g., CCD 106 and/orcomputing device 108) or be input by a user via a user interface element540 associated with the infusion device 502. In practice, the one ormore user interface element(s) 540 associated with the infusion device502 typically include at least one input user interface element, suchas, for example, a button, a keypad, a keyboard, a knob, a joystick, amouse, a touch panel, a touchscreen, a microphone or another audio inputdevice, and/or the like. Additionally, the one or more user interfaceelement(s) 508 include at least one output user interface element, suchas, for example, a display element (e.g., a light-emitting diode or thelike), a display device (e.g., a liquid crystal display or the like), aspeaker or another audio output device, a haptic feedback device, or thelike, for providing notifications or other information to the user. Itshould be noted that although FIG. 5 depicts the user interfaceelement(s) 540 as being separate from the infusion device 502, inpractice, one or more of the user interface element(s) 508 may beintegrated with the infusion device 502. Furthermore, in someembodiments, one or more user interface element(s) 540 are integratedwith the sensing arrangement 504 in addition to and/or in alternative tothe user interface element(s) 508 integrated with the infusion device502.

Still referring to FIG. 5, in the illustrated embodiment, the infusiondevice 502 includes a motor control module 512 coupled to a motor 507(e.g., motor assembly 207) that is operable to displace a plunger 517(e.g., plunger 217) in a reservoir (e.g., reservoir 205) and provide adesired amount of fluid to the body 501 of a user. In this regard,displacement of the plunger 517 results in the delivery of a fluid thatis capable of influencing the condition in the body 501 of the user tothe body 501 of the user via a fluid delivery path (e.g., via tubing 221of an infusion set 225). A motor driver module 514 is coupled between anenergy source 503 and the motor 507. The motor control module 512 iscoupled to the motor driver module 514, and the motor control module 512generates or otherwise provides command signals that operate the motordriver module 514 to provide current (or power) from the energy source503 to the motor 507 to displace the plunger 517 in response toreceiving, from a pump control system 520, a dosage command indicativeof the desired amount of fluid to be delivered.

In exemplary embodiments, the energy source 503 is realized as a batteryhoused within the infusion device 502 (e.g., within housing 202) thatprovides direct current (DC) power. In this regard, the motor drivermodule 514 generally represents the combination of circuitry, hardwareand/or other electrical components configured to convert or otherwisetransfer DC power provided by the energy source 503 into alternatingelectrical signals applied to respective phases of the stator windingsof the motor 507 that result in current flowing through the statorwindings that generates a stator magnetic field and causes the rotor ofthe motor 507 to rotate. The motor control module 512 is configured toreceive or otherwise obtain a commanded dosage from the pump controlsystem 520, convert the commanded dosage to a commanded translationaldisplacement of the plunger 517, and command, signal, or otherwiseoperate the motor driver module 514 to cause the rotor of the motor 507to rotate by an amount that produces the commanded translationaldisplacement of the plunger 517. For example, the motor control module512 may determine an amount of rotation of the rotor required to producetranslational displacement of the plunger 517 that achieves thecommanded dosage received from the pump control system 520. Based on thecurrent rotational position (or orientation) of the rotor with respectto the stator that is indicated by the output of the rotor sensingarrangement 516, the motor control module 512 determines the appropriatesequence of alternating electrical signals to be applied to therespective phases of the stator windings that should rotate the rotor bythe determined amount of rotation from its current position (ororientation). In embodiments where the motor 507 is realized as a BLDCmotor, the alternating electrical signals commutate the respectivephases of the stator windings at the appropriate orientation of therotor magnetic poles with respect to the stator and in the appropriateorder to provide a rotating stator magnetic field that rotates the rotorin the desired direction. Thereafter, the motor control module 512operates the motor driver module 514 to apply the determined alternatingelectrical signals (e.g., the command signals) to the stator windings ofthe motor 507 to achieve the desired delivery of fluid to the user.

When the motor control module 512 is operating the motor driver module514, current flows from the energy source 503 through the statorwindings of the motor 507 to produce a stator magnetic field thatinteracts with the rotor magnetic field. In some embodiments, after themotor control module 512 operates the motor driver module 514 and/ormotor 507 to achieve the commanded dosage, the motor control module 512ceases operating the motor driver module 514 and/or motor 507 until asubsequent dosage command is received. In this regard, the motor drivermodule 514 and the motor 507 enter an idle state during which the motordriver module 514 effectively disconnects or isolates the statorwindings of the motor 507 from the energy source 503. In other words,current does not flow from the energy source 503 through the statorwindings of the motor 507 when the motor 507 is idle, and thus, themotor 507 does not consume power from the energy source 503 in the idlestate, thereby improving efficiency.

Depending on the embodiment, the motor control module 512 may beimplemented or realized with a general purpose processor, amicroprocessor, a controller, a microcontroller, a state machine, acontent addressable memory, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof, designed to perform the functions described herein.Furthermore, the steps of a method or algorithm described in connectionwith the embodiments disclosed herein may be embodied directly inhardware, in firmware, in a software module executed by the motorcontrol module 512, or in any practical combination thereof. Inexemplary embodiments, the motor control module 512 includes orotherwise accesses a data storage element or memory, including any sortof random access memory (RAM), read only memory (ROM), flash memory,registers, hard disks, removable disks, magnetic or optical massstorage, or any other short or long term storage media or othernon-transitory computer-readable medium, which is capable of storingprogramming instructions for execution by the motor control module 512.The computer-executable programming instructions, when read and executedby the motor control module 512, cause the motor control module 512 toperform the tasks, operations, functions, and processes describedherein.

It should be appreciated that FIG. 5 is a simplified representation ofthe infusion device 502 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. In this regard,depending on the embodiment, some features and/or functionality of thesensing arrangement 504 may implemented by or otherwise integrated intothe pump control system 520, or vice versa. Similarly, in practice, thefeatures and/or functionality of the motor control module 512 mayimplemented by or otherwise integrated into the pump control system 520,or vice versa. Furthermore, the features and/or functionality of thepump control system 520 may be implemented by control electronics 224located in the fluid infusion device 200, 400, while in alternativeembodiments, the pump control system 520 may be implemented by a remotecomputing device that is physically distinct and/or separate from theinfusion device 502, such as, for example, the CCD 106 or the computingdevice 108. Additionally, although FIG. 5 depicts the sensingarrangement 504 as being physically separate and distinct from theinfusion device 502, in alternative embodiments, the sensing arrangement504 may be integrated into or otherwise implemented by the infusiondevice 502 (e.g., by providing the sensing arrangement 504 within thehousing 202). Furthermore, more complex control schemes may beimplemented by the pump control system 520 with multiple sensingarrangements 504 being utilized in conjunction with one another.

FIG. 6 depicts an exemplary embodiment of an electronic device 600suitable for use as the sensing arrangement 504 of FIG. 5 in accordancewith one or more embodiments. For purposes of explanation, but withoutlimitation, the device 600 may alternatively be referred to herein as asensing device or a sensing arrangement. The illustrated sensing device600 includes, without limitation, a control module 602, a sensingelement 604, an output interface 606, and a data storage element (ormemory) 608. The control module 602 is coupled to the sensing element604, the output interface 606, and the memory 608, and the controlmodule 602 is suitably configured to support the operations, tasks,and/or processes described herein.

The sensing element 604 generally represents the component of thesensing device 600 that is configured to generate, produce, or otherwiseoutput one or more electrical signals indicative of a condition that issensed, measured, or otherwise quantified by the sensing device 600. Inthis regard, the physiological condition of a user influences acharacteristic of the electrical signal output by the sensing element604, such that the characteristic of the output signal corresponds to oris otherwise correlative to the physiological condition that the sensingelement 604 is sensitive to. For example, referring to FIG. 5, thesensing element 604 may be realized as a glucose sensing element thatgenerates an output electrical signal having a current (or voltage)associated therewith that is correlative to the interstitial fluidglucose level that is sensed or otherwise measured in the body 501 ofthe user by the sensing arrangement 504, 600.

Still referring to FIG. 6, the control module 602 generally representsthe hardware, circuitry, logic, firmware and/or other component(s) ofthe sensing device 600 that is coupled to the sensing element 604 toreceive the electrical signals output by the sensing element 604 andperform various additional tasks, operations, functions and/or processesdescribed herein. For example, in one or more embodiments, the controlmodule 602 implements or otherwise executes a data managementapplication module 610 that filters, analyzes or otherwise processes theelectrical signals received from the sensing element 604 to obtain afiltered measurement value indicative of the measured interstitial fluidglucose level, as described in greater detail below in the context ofFIGS. 7-11. Additionally, in one or more embodiments, the control module602 also implements or otherwise executes a calibration applicationmodule 612 that calculates or otherwise determines a calibration factorfor converting the filtered measurement value from the data managementapplication 610 to a sensed glucose value based at least in part on oneor more filtered measurement values from the data management application610 and corresponding reference blood glucose measurement values (e.g.,from blood glucose meter 530) paired with those filtered measurementvalues, as described in greater detail below in the context of FIGS.12-14. In some embodiments, the control module 602 also implements orotherwise executes a health monitoring application module 614 thatdetects or otherwise identifies replacement or other maintenance withrespect to the sensing element 604 is desirable based on signalcharacteristics associated with the output electrical signals from thesensing element 604, as described in greater detail below in the contextof FIG. 15.

Depending on the embodiment, the control module 602 may be implementedor realized with a general purpose processor, a microprocessor, acontroller, a microcontroller, a state machine, a content addressablememory, an application specific integrated circuit, a field programmablegate array, any suitable programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof, designed to perform the functions described herein. In thisregard, the steps of a method or algorithm described in connection withthe embodiments disclosed herein may be embodied directly in hardware,in firmware, in a software module executed by the control module 602, orin any practical combination thereof.

In some embodiments, the control module 602 includes ananalog-to-digital converter (ADC) or another similar samplingarrangement that samples or otherwise converts the output electricalsignal received from the sensing element 604 into corresponding digitalmeasurement data value. In other embodiments, the sensing element 604may incorporate an ADC and output a digital measurement value. Forpurposes of explanation, the input to the data management application610 from the sensing element 604 may alternatively be referred to hereinas the unfiltered measurement value, which should be understood asreferring to the digital value correlative to the interstitial fluidglucose level sensed by the sensing element 604. In one or moreembodiments, the current of the electrical signal output by the sensingelement 604 is influenced by the user's interstitial fluid glucoselevel, and the input to the data management application 610 is realizedas an unfiltered current measurement value. As described above,depending on the embodiment, the unfiltered measurement value may beoutput directly by the sensing element 604 or converted based on ananalog electrical output signal from the sensing element 604 by an ADCof the control module 602.

In exemplary embodiments, the control module 602 includes or otherwiseaccesses the data storage element or memory 608. The memory 608 may berealized using any sort of RAM, ROM, flash memory, registers, harddisks, removable disks, magnetic or optical mass storage, short or longterm storage media, or any other non-transitory computer-readable mediumcapable of storing programming instructions, code, or other data forexecution by the control module 602. The computer-executable programminginstructions, when read and executed by the control module 602, causethe control module 602 to implement or otherwise generate theapplications 610, 612, 614 and perform the tasks, operations, functions,and processes described in greater detail below.

The output interface 606 generally represents the hardware, circuitry,logic, firmware and/or other components of the sensing device 600 thatare coupled to the control module 602 for outputting data and/orinformation from/to the sensing device 600 to/from the infusion device502, the pump control system 520 and/or the user. In this regard, inexemplary embodiments, the output interface 606 is realized as acommunications interface configured to support communications to/fromthe sensing device 600. In such embodiments, the communicationsinterface 606 may include or otherwise be coupled to one or moretransceiver modules capable of supporting wireless communicationsbetween the sensing device 600 and another electronic device (e.g., aninfusion device 102, 502 or another electronic device 106, 108 in aninfusion system 100). Alternatively, the communications interface 606may be realized as a port that is adapted to receive or otherwise becoupled to a wireless adapter that includes one or more transceivermodules and/or other components that support the operations of thesensing device 600 described herein. In other embodiments, thecommunications interface 606 may be configured to support wiredcommunications to/from the sensing device 600. In yet other embodiments,the output interface 606 may include or otherwise be realized as anoutput user interface element, such as a display element (e.g., alight-emitting diode or the like), a display device (e.g., a liquidcrystal display or the like), a speaker or another audio output device,a haptic feedback device, or the like, for providing notifications orother information to the user. In such embodiments, the output userinterface 606 may be integrated with the sensing arrangement 504, 600(e.g., within a common housing) or implemented separately (e.g., userinterface element 540).

It should be understood that FIG. 6 is a simplified representation of asensing device 600 for purposes of explanation and is not intended tolimit the subject matter described herein in any way. In this regard,although FIG. 6 depicts the various elements residing within the sensingdevice 600, one or more elements of the sensing device 600 may bedistinct or otherwise separate from the other elements of the sensingdevice 600. For example, the sensing element 604 may be separate and/orphysically distinct from the control module 602 and/or thecommunications interface 606. Furthermore, although FIG. 6 depicts theapplications 610, 612, 614 as being implemented by the sensing device600, in alternative embodiments, features and/or functionality of one ormore of the applications 610, 612, 614 may be implemented by orotherwise reside on the infusion device 102, 502 or another device 106,108 within an infusion system 100. For example, in some embodiments, thefeatures and/or functionality of one or more of the applications 610,612, 614 may be implemented by the pump control system 520.

FIG. 7 depicts an exemplary embodiment of a signal processing system 700suitable for implementation by the data management application 610 inthe sensing arrangement 600 of FIG. 6 in accordance with one or moreembodiments. The illustrated signal processing system 700 includes,without limitation, an unfiltered sample buffer 702, a first filteringmodule 704, an artifact detection module 706, a filtered sample buffer708, a signal analysis module 710, a dropout detection module 712, and asecond filtering module 714. It should be understood that FIG. 7 is asimplified representation of a signal processing system 700 for purposesof explanation and is not intended to limit the subject matter describedherein in any way. In this regard, practical embodiments of the signalprocessing system 700 may include additional components and/or elementsconfigured to perform additional signal processing features and/orfunctionality which are not described herein.

The unfiltered sample buffer 702 generally represents a data storageelement (e.g., a particular allocated portion of memory 608) that iscoupled to the sensing element 604 (e.g., via an ADC) and configured tostore or otherwise maintain a plurality of unfiltered measurement valuesmost recently obtained from the sensing element 604. In one or moreexemplary embodiments, an unfiltered measurement value is obtained fromthe sensing element 604 on a per-minute basis (e.g., by periodicallysampling the output of the sensing element 604 once every minute), withthe unfiltered sample buffer 702 storing the 8 most recent unfilteredmeasurement values.

The first filtering module 704 accesses the unfiltered sample buffer 702to obtain the most recent unfiltered measurement values and low-passfilters the unfiltered measurement values to obtain a filteredmeasurement value corresponding to the most recent sampling time. Inexemplary embodiments, the low-pass filtering module 704 appliesasymmetric finite impulse response (FIR) filter coefficients to the 8most recent unfiltered measurement values to obtain a filteredmeasurement value corresponding to the most recent sampling time. Inthis regard, for samples obtained on a per-minute basis, the filtercoefficients may be chosen to provide a group delay of about one minuteor less. In other words, the group delay is less than the samplingperiod. In exemplary embodiments, the low-pass filtering module 704obtains the unfiltered measurement values from the unfiltered samplebuffer 702 on a periodic basis at a frequency that is less than or equalto the rate at which the unfiltered sample buffer 702 is updated. Forexample, the unfiltered sample buffer 702 may be updated once everyminute while the low-pass filtering module 704 filters the unfilteredmeasurement values once every five minutes. Accordingly, for purposes ofexplanation, a filtered measurement value output by the low-passfiltering module 704 may alternatively be referred to herein as afive-minute filtered measurement value.

The artifact detection module 706 is coupled to the output of the firstfiltering module 704 and analyzes the filtered measurement value todetermine whether or not the filtered measurement value is indicative ofan artifact in one or more of the unfiltered measurement values beforeproviding the filtered measurement value to the filtered sample buffer708. In this regard, the artifact detection module 706 detects orotherwise identifies when the magnitude of the change in the five-minutefiltered measurement value relative to the preceding five-minutefiltered measurement value is unlikely to be exhibited in the body of auser, as described in greater detail below in the context of FIG. 8. Inthis regard, the filtered sample buffer 708 also maintains, inassociation with each respective five-minute filtered measurement value,an indication of whether or not that filtered measurement value is validand usable by the second filtering module 714, the pump control system520 and/or other components in the control system 500. Additionally, theartifact detection module 706 may analyze the filtered measurement valueto verify or otherwise confirm the filtered measurement value is withinan acceptable range of values, and flag or otherwise mark any filteredmeasurement value outside of the acceptable range of values as beinginvalid or otherwise unusable.

In exemplary embodiments, the filtered sample buffer 708 stores orotherwise maintains a plurality of filtered measurement values. Forexample, in one or more embodiments, the filtered sample buffer 708stores the eight most recent five-minute filtered measurement values. Asdescribed in greater detail below in the context of FIG. 9, the signalanalysis module 710 accesses the filtered measurement values in thefiltered sample buffer 708 and analyzes the filtered measurement valuesto calculate or otherwise identify one or more metrics indicative ofsignal characteristics associated with the five-minute filteredmeasurement signal. In exemplary embodiments, the signal analysis module710 determines an estimate of the signal noise (or noise metric)associated with the filtered measurement values at the respectivesampling time of a respective filtered measurement value along with anestimate of the signal frequency (or frequency metric) associated withthe filtered measurement values. As described in greater detail below inthe context of FIG. 11, the second filtering module 714 is coupled tothe signal analysis module 710 to obtain the signal characteristicmetrics determined by the signal analysis module 710 and adaptivelyfilter the most recent five-minute filtered measurement value in thefiltered sample buffer 708 based at least in part on the signalcharacteristic metrics. In one embodiment, the second filtering module714 implements a Kalman filter that filters the filtered measurementvalue input to the adaptive filtering module 714 from the buffer 708using the measurement value previously output by the adaptive filteringmodule 714 and the signal characteristic metrics from the signalanalysis module 710. In exemplary embodiments, the adaptive filteringmodule 714 is also coupled to the dropout detection module 712, which isconfigured to detect or otherwise identify dropouts in the five-minutefiltered measurement signal as described in greater detail below in thecontext of FIG. 10. In this regard, in response to detecting a dropoutcondition, the adaptive filtering module 714 is configured to adjust orotherwise modify the amount of filtering to account for the dropoutcondition.

FIG. 8 depicts an exemplary artifact detection process 800 suitable forimplementation by the sensing arrangement 504, 600 to detect orotherwise identify invalid or otherwise unusable measurement values. Thevarious tasks performed in connection with the artifact detectionprocess 800 may be performed by hardware, firmware, software executed byprocessing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1-7. In practice, portions of the artifactdetection process 800 may be performed by different elements of thesensing arrangement 504, 600 and/or control system 500. That said, inexemplary embodiments described herein, the artifact detection process800 is performed by the artifact detection module 706 of the datamanagement application 610 implemented by the control module 602. Itshould be appreciated that the artifact detection process 800 mayinclude any number of additional or alternative tasks, the tasks neednot be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the artifact detection process 800 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 8 couldbe omitted from a practical embodiment of the artifact detection process800 as long as the intended overall functionality remains intact.

In exemplary embodiments, the artifact detection process 800 initializesor otherwise begins by receiving or otherwise obtaining a new filteredmeasurement value and verifying or otherwise confirming the filteredmeasurement value is not invalid (task 802). In this regard, theartifact detection process 800 receives a new filtered measurement valuefrom the low-pass filtering module 704 and confirms that the filteredmeasurement value is within an acceptable range of values. For example,in one embodiment, the artifact detection process 800 confirms thefiltered measurement value corresponds to a current through the sensingelement 604 that is greater than a minimum acceptable threshold currentthreshold value and less than a maximum acceptable threshold currentvalue. When it is determined that the filtered measurement value is notwithin the acceptable range of values, the artifact detection process800 determines the filtered measurement value is invalid or otherwiseshould not be utilized for subsequent calculations and marks orotherwise indicates the filtered measurement value as being invalid(task 818). In this regard, when the artifact detection process 800determines a five-minute filtered measurement value is not within therange of acceptable values, the artifact detection process 800 storesthat five-minute filtered measurement value in the buffer 708 inassociation with an indicator or flag that designates the filteredmeasurement value is invalid or otherwise unusable.

When the filtered measurement value is otherwise acceptable, theillustrated process 800 continues by calculating or otherwisedetermining one or more artifact deviation thresholds and identifyingwhether a difference between the filtered measurement value and arespective preceding filtered measurement value is greater than anapplicable artifact deviation threshold (tasks 804, 806). In thisregard, an artifact deviation threshold represents a change in thefiltered measurement value over a particular amount of time that isunlikely to be exhibited in the body 501 of the user and is most likelyattributable to an artifact in the output signal generated by thesensing element 604. For example, in one embodiment, the artifactdetection module 706 calculates the artifact deviation threshold betweenthe instant filtered measurement value and the preceding filteredmeasurement value by multiplying the preceding measurement value by apercentage indicative of a change over the time difference betweensuccessive samples (e.g., 5 minutes) that is likely to be attributableto an artifact. Similarly, the artifact detection module 706 maycalculate a ten minute artifact deviation threshold between the currentfiltered measurement value and the second preceding filtered measurementvalue (e.g., from two samples ago) by multiplying that precedingmeasurement value by a larger percentage indicative of a change betweena larger time difference (e.g., 10 minutes) that is likely to beattributable to an artifact, and so on. In this manner, one or more ofthe artifact deviation threshold values may vary dynamically based onthe magnitude(s) of one or more of the preceding filtered measurementvalues. In some embodiments, the artifact detection module 706 may alsoapply one or more fixed artifact deviation thresholds to thedifference(s) between the current filtered measurement value and one ormore preceding filtered measurement values. For example, the artifactdetection module 706 may detect an artifact when the difference betweenthe current filtered measurement value and the preceding filteredmeasurement value is greater than a first artifact deviation thresholdcurrent value, or when the difference between the current filteredmeasurement value and the second preceding filtered measurement valueexceeds a greater artifact deviation threshold current value, and so on.

When the differences between the current filtered measurement value andthe preceding filtered measurement value(s) are less than the applicableartifact deviation threshold(s), the artifact detection process 800marks or otherwise indicates the current filtered measurement value isvalid or otherwise usable for subsequent calculations (task 816). Inthis regard, the artifact detection module 706 may store the five-minutefiltered measurement value in the buffer 708 in association with anindicator or flag that designates the filtered measurement value asbeing valid and usable. Conversely, in response to determining adifference between the current filtered measurement value and aparticular preceding filtered measurement value exceeds the applicableartifact deviation value corresponding to the time difference betweenthose two filtered measurement values, the artifact detection process800 continues by marking or otherwise indicating the current filteredmeasurement value as being invalid (task 808). In a similar manner asdescribed above, the artifact detection module 706 may store thefiltered measurement value in the buffer 708 in association with anindicator or flag that designates the filtered measurement value isinvalid or otherwise unusable. In some embodiments, the artifactdetection process 800 may exit after marking the current filteredmeasurement value as invalid and reinitialize on the next subsequentfiltered measurement value. For example, in one embodiment, when thedifference between the current filtered measurement value and thepreceding filtered measurement value is greater than first percentage ofthe preceding filtered measurement value but less than a largerpercentage of the preceding filtered measurement value, the artifactdetection process 800 may indicate the current filtered measurementvalue is invalid and exit.

In the illustrated embodiment, the artifact detection process 800receives or otherwise obtains the next subsequent filtered measurementvalue, calculates or otherwise determines a recovery value based on themost recent valid filtered measurement value and the time elapsed sincethe most recent valid filtered measurement value, and verifies orotherwise confirms that subsequently filtered measurement value isgreater than the recovery value (tasks 810, 812, 814). In this regard,the artifact detection process 800 may persistently mark subsequentfiltered measurement values as invalid until a filtered measurementvalue indicates the filtered measurement signal has recovered from theartifact condition (task 808). In some embodiments, the recovery valuedynamically decreases as the amount of time elapsed since detecting theartifact condition increases. For example, for the first sampleimmediately following detection of an artifact, the artifact detectionmodule 706 may calculate or otherwise determine the recovery value isequal to 75% of the most recent valid filtered measurement value. Ifthat filtered measurement value is greater than 75% of the most recentvalid filtered measurement value, the artifact detection module 706stores that filtered measurement value in the buffer 708 in associationwith an indication that that filtered measurement value is valid.Otherwise, the artifact detection module 706 stores that filteredmeasurement value in the buffer 708 in association with an indicator orflag that designates it as being invalid. For the following sample(s),the artifact detection module 706 may calculate or otherwise determinethe recovery value is equal to a reduced percentage (e.g., 60%) of themost recent valid filtered measurement value (or alternatively, afraction of the preceding recovery value), and so on, therebydynamically and progressively decreasing the recovery value. Once afiltered measurement value exceeds the recovery value and indicatesrecovery from the artifact condition, the artifact detection process 800marks or otherwise indicates that the instant filtered measurement valueis valid (task 816). In some embodiments, the artifact detection module706 may impose a timer or limit on the duration for which the invalidindication is persisted. For example, if more than a threshold number ofsamples have occurred since the most recent valid filtered measurementvalue, the artifact detection process 800 may mark or otherwise indicatethe instant five-minute filtered measurement value as being valid (e.g.,task 816) even though it may not exceed the current recovery value.

FIG. 9 depicts an exemplary signal analysis process 900 suitable forimplementation by the sensing arrangement 504, 600 to calculate orotherwise determine metrics indicative of signal characteristicsassociated with a measurement signal. The various tasks performed inconnection with the signal analysis process 900 may be performed byhardware, firmware, software executed by processing circuitry, or anycombination thereof. For illustrative purposes, the followingdescription refers to elements mentioned above in connection with FIGS.1-7. In practice, portions of the signal analysis process 900 may beperformed by different elements of the sensing arrangement 504, 600and/or control system 500. That said, in exemplary embodiments describedherein, the signal analysis process 900 is performed by the signalanalysis module 710 of the data management application 610 implementedby the control module 602. It should be appreciated that the signalanalysis process 900 may include any number of additional or alternativetasks, the tasks need not be performed in the illustrated order and/orthe tasks may be performed concurrently, and/or the signal analysisprocess 900 may be incorporated into a more comprehensive procedure orprocess having additional functionality not described in detail herein.Moreover, one or more of the tasks shown and described in the context ofFIG. 9 could be omitted from a practical embodiment of the signalanalysis process 900 as long as the intended overall functionalityremains intact.

In the illustrated embodiment, the signal analysis process 900calculates or otherwise determines a first derivative metric associatedwith the instant (or most recent) filtered measurement value (task 902).In this regard, the signal analysis module 710 determines the value ofthe first derivative associated with the current filtered measurementvalue in the buffer 708 based on the difference between the currentfiltered measurement value and the immediately preceding filteredmeasurement value in the buffer 708 (e.g., by subtracting the precedingfiltered measurement value from the current filtered measurement value).In one or more embodiments, the signal analysis module 710 stores orotherwise maintains the first derivative value associated with eachrespective filtered measurement value in the buffer 708 or elsewhere inmemory 608. In exemplary embodiments, the signal analysis module 710calculates the first derivative metric associated with the currentfiltered measurement value by averaging the respective first derivativevalues associated with each of the five-minute filtered measurementvalues in the buffer 708, then determining the absolute value (ormagnitude) of the average first derivative value. Thus, the firstderivative metric associated with the current filtered measurement valuecorresponds to the absolute value of the average for the firstderivative of the filtered measurement signal over the precedinginterval of time corresponding to the measurement values in the buffer708 (e.g., the preceding 40 minutes for the case of 8 samples obtainedat 5 minute intervals). That said, in alternative embodiments, the firstderivative value associated with the current filtered measurement value(e.g., the difference between the current filtered measurement value andthe immediately preceding filtered measurement value) may be utilized asthe first derivative metric rather than averaging all of the firstderivative values associated with five-minute filtered measurementvalues in the buffer 708. It should be noted that in practice, anymeasurement values in the buffer 708 that are flagged as being invalidmay be excluded from the calculations when determining the firstderivative metric. In this regard, in some embodiments, the signalanalysis module 710 may perform interpolation or another similartechnique to account for invalid measurements in the buffer 708.

In a similar manner, the signal analysis process 900 also calculates orotherwise determines a second derivative metric associated with theinstant (or most recent) filtered measurement value (task 904). In thisregard, the signal analysis module 710 determines the value of thesecond derivative associated with the most recent five-minute filteredmeasurement value in the buffer 708 based on the difference between thefirst derivative value associated with the most recent filteredmeasurement value and the first derivative associated with theimmediately preceding filtered measurement value in the buffer 708(e.g., by subtracting the first derivative value associated with thepreceding filtered measurement value from the first derivative valueassociated with the instant filtered measurement value). In one or moreembodiments, the signal analysis module 710 also stores or otherwisemaintains the second derivative value associated with each respectivefiltered measurement value in the buffer 708 or elsewhere in memory 608.In exemplary embodiments, the signal analysis module 710 calculates thesecond derivative metric associated with the current filteredmeasurement value by determining the average magnitude of the respectivesecond derivative values associated with each of the five-minutefiltered measurement values in the buffer 708. Before averaging thesecond derivative values, the absolute value (or magnitude) of thesecond derivative values is obtained. Thus, the second derivative metricassociated with the current filtered measurement value corresponds tothe average magnitude for the second derivative of the filteredmeasurement signal over the preceding interval of time corresponding tothe measurement values in the buffer 708. That said, in someembodiments, the second derivative value associated with the currentfiltered measurement value (e.g., the difference between the firstderivative values for the most recent five-minute filtered measurementvalue and the immediately preceding five-minute filtered measurementvalue) may be utilized as the second derivative metric without averagingall of the second derivative values. For example, in some embodiments,the greater of the second derivative value associated with the instantfiltered measurement value and the average magnitude of the secondderivative values associated with all of the filtered measurement valuesin the buffer 708 is used as the second derivative metric. As describedabove, in practice, measurement values in the buffer 708 that areflagged as being invalid may be excluded from the calculations whendetermining the second derivative metric, and first derivative valuesdetermined using interpolation or another similar technique may beutilized to account for invalid measurements in the buffer 708.

The signal analysis process 900 continues by calculating or otherwisedetermining an estimate of the frequency of the filtered measurementsignal based at least in part on the first derivative metric (task 906).For example, in accordance with one embodiment, the signal analysismodule 710 determines an estimated signal frequency metric bymultiplying or otherwise scaling the first derivative metric by thecalibration factor currently being utilized to convert the output fromthe adaptive filtering module 714 into a blood glucose value, and thenclips the result such that the estimated signal frequency metric doesnot exceed an upper limit (e.g., 4). In some embodiments, the signalanalysis module 710 may also impose a floor so that the estimated signalfrequency does not fall below a lower limit (e.g., 0.2).

The signal analysis process 900 also calculates or otherwise determinesan estimate of the noise present in the filtered measurement signalbased at least in part on the second derivative metric (task 908). In asimilar manner as described above, the signal analysis module 710determines an estimated signal noise metric by multiplying or otherwisescaling the second derivative metric by the current calibration factor,and then clips the result such that the estimated signal noise metricdoes not exceed 10. In one or more embodiments, the signal analysismodule 710 determines the estimated signal noise metric using the largerof the second derivative value associated with the current filteredmeasurement value and the second derivative metric associated with thecurrent filtered measurement value. In this regard, when the secondderivative value associated with the current filtered measurement valueis greater than the average magnitude for the second derivative of thefiltered measurement signal over the preceding interval of timecorresponding to the measurement values in the buffer 708, the signalanalysis module 710 determines the estimated signal noise metric bymultiplying or otherwise scaling the second derivative value by thecurrent calibration factor and clipping the result instead of using theaveraged second derivative value.

FIG. 10 depicts an exemplary dropout detection process 1000 suitable forimplementation by the sensing arrangement 504, 600 to detect orotherwise identify presence of a dropout condition in the measurementsignal. The various tasks performed in connection with the dropoutdetection process 1000 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-7. In practice, portions ofthe dropout detection process 1000 may be performed by differentelements of the sensing arrangement 504, 600 and/or control system 500.That said, in exemplary embodiments described herein, the dropoutdetection process 1000 is performed by the dropout detection module 712of the data management application 610 implemented by the control module602. It should be appreciated that the dropout detection process 1000may include any number of additional or alternative tasks, the tasksneed not be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the dropout detection process 1000 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 10 couldbe omitted from a practical embodiment of the dropout detection process1000 as long as the intended overall functionality remains intact.

In exemplary embodiments, the dropout detection process 1000 determinesa first derivative metric associated with the instant (or most recent)filtered measurement value and detects or otherwise identifies a dropoutcondition when the first derivative metric is indicative of a relativelylarge drop or a relatively quick drop in the filtered measurement value(tasks 1002, 1004). When the first derivative metric associated with theinstant filtered measurement value is indicative of a relatively largeor relatively quick drop in the filtered measurement signal, the dropoutdetection process 1000 detects a dropout condition and generates orotherwise provides indication of the dropout condition (task 1006). Inthis regard, the dropout detection module 712 provides a signal ornotification to the adaptive filtering module 714 identifying the likelypresence of a dropout condition, and in response, the adaptive filteringmodule 714 dynamically adjusts the filtering of the filtered measurementsignal in a manner that mitigates or otherwise remediates the dropoutcondition, as described in greater detail below.

In one embodiment, the dropout detection module 712 identifies a dropoutcondition when the first derivative metric is greater than a firstthreshold value and a second derivative metric is less than a secondthreshold value having magnitude of the second threshold value is lessthan the first threshold value, such that the dropout detection module712 identifies a dropout condition when the filtered measurement signalabruptly drops to an abnormally low reading that is more likely to beattributable to transient behavior in the user's body or the sensingelement 604 than an artifact condition. In one or more embodiments, thedropout detection module 712 obtains the first and second derivativemetrics determined by the signal analysis module 710 and stored inassociation with the instant five-minute filtered measurement in thebuffer 708 and/or memory 608. In alternative embodiments, the dropoutdetection module 712 calculates the first and second derivative metricsindependently.

In exemplary embodiments, the dropout detection module 712 calculates orotherwise determines the first derivative metric by multiplying adifference between the instant filtered measurement value and thepreceding filtered measurement value (e.g., the first derivativeassociated with the instant filtered measurement value) by the currentcalibration factor. The dropout detection module 712 may also calculateor otherwise determine the second derivative metric by multiplying thesecond derivative associated with the instant filtered measurement value(e.g., a difference between the derivative associated with the instantfiltered measurement value and the derivative associated with theimmediately preceding filtered measurement value) by the currentcalibration factor. In one embodiment, the dropout detection module 712detects a dropout condition in response to a relatively large drop inthe filtered measurement signal when the first derivative metricassociated with the instant filtered measurement value is less than −5mg/dL/min while the absolute value (or magnitude) of the second metricassociated with the instant filtered measurement value is less than 1mg/dL/min/min and the absolute value (or magnitude) first derivativemetric associated with the immediately preceding filtered measurementvalue is less than 0.75 mg/dL/min. The dropout detection module 712 mayalso detects a dropout condition in response to a relatively moderatedrop in the filtered measurement signal when the first derivative metricassociated with the instant filtered measurement value is less than−0.75 mg/dL/min while the noise metric (e.g., from task 908) associatedwith the filtered measurement signal is less than 1 and the firstderivative metric associated with the immediately preceding filteredmeasurement value is greater than −0.5 mg/dL/min. Additionally, thedropout detection module 712 may also detect a dropout condition inresponse to a relatively quick change in the direction of the filteredmeasurement signal when the first derivative metric associated with theinstant filtered measurement value is less than −2.5 mg/dL/min while thenoise metric (e.g., from task 908) associated with the filteredmeasurement signal is less than 1 and the first derivative metricassociated with the immediately preceding filtered measurement value isgreater than 0 mg/dL/min.

FIG. 11 depicts an exemplary adaptive filtering process 1100 suitablefor implementation by the sensing arrangement 504, 600 to detect orotherwise identify presence of a dropout condition in the measurementsignal. The various tasks performed in connection with the adaptivefiltering process 1100 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-7. In practice, portions ofthe adaptive filtering process 1100 may be performed by differentelements of the sensing arrangement 504, 600 and/or control system 500.That said, in exemplary embodiments described herein, the adaptivefiltering process 1100 is performed by the adaptive filtering module 714of the data management application 610 implemented by the control module602. It should be appreciated that the adaptive filtering process 1100may include any number of additional or alternative tasks, the tasksneed not be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the adaptive filtering process 1100 maybe incorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 11 couldbe omitted from a practical embodiment of the adaptive filtering process1100 as long as the intended overall functionality remains intact.

Referring to FIG. 11, and with continued reference to FIGS. 5-10, inexemplary embodiments, the adaptive filtering process 1100 is performedeach time the buffer 708 is updated with a new filtered measurementvalue (e.g., once every 5 minutes). The illustrated process 1100 beginsby verifying or otherwise confirming the current (or most recent)filtered measurement value is valid and usable (task 1102). In thisregard, the adaptive filtering module 714 verifies the newest filteredmeasurement value in the buffer 708 is not flagged or otherwise markedas invalid before proceeding with processing the value. When the currentfiltered measurement value is invalid, the adaptive filtering process1100 maintains the same output measurement value and error estimate thatwere output and fed back from the preceding iteration of the adaptivefiltering process 1100 (task 1120), as described in greater detailbelow. In this regard, the adaptive filtering module 714 outputs themeasurement value and error estimate that were fed back from theprevious iteration without performing any processing on the invalidmeasurement value. Additionally, in one or more embodiments, when thecurrent filtered measurement value is the initial measurement valueinput to the adaptive filtering module 714 (e.g., the first value afterpowering on and/or resetting the sensing device 600), the adaptivefiltering process 1100 outputs the current filtered measurement valueand exits without any further processing on the current filteredmeasurement value. It should be noted that in some embodiments, wherethe infusion device 502 and/or pump control system 520 are configured topresent a graphical representation of the user's sensed glucose value ona display (e.g., user interface element 540), maintaining the previouslyoutput measurement for use in determining the sensed glucose valueimproves the user experience by eliminating or otherwise reducing thevisibility of rapid and/or large transient changes in the displayedsensed glucose value that could otherwise alarm the user or cause theuser to take unnecessary action(s).

When the current filtered measurement value is valid and not the initialmeasurement value input to the adaptive filtering module 714, theadaptive filtering process 1100 continues by calculating or otherwisedetermining a value for a process variance metric based on the estimatesof the signal characteristics for the filtered measurement signal (task1104). In this regard, the adaptive filtering module 714 calculates theprocess variance metric based on the estimates of the signal frequencyand signal noise associated with the filtered measurement signalobtained from the signal analysis module 710. In one embodiment, theadaptive filtering module 714 calculates a first variable (c₁) based onthe signal frequency estimate (f_(e)) using equation c₁=a₁×f_(e)−b₁, andcalculates a second variable (c₂) based on the signal frequency estimate(f_(e)) using equation c₂=a₂ ln(f_(e))−b₂. The adaptive filtering module714 continues by calculating the process variance metric (Q) based onthe signal frequency estimate (f_(e)) and the signal noise estimate(n_(e)) using equation Q=c₁×e^((c) ² ^(×n) ^(e) ⁾. In exemplaryembodiments, a₁, a₂, b₁ and b₂ are scalar values chosen to reduce thedifference (or error) between the output of the adaptive filteringmodule 714 and a known reference input signal (e.g., a sine wavesignal).

The illustrated process 1100 continues by identifying or otherwisedetermining whether a dropout condition has been detected, and in theabsence of a dropout condition, adjusting the input filtered measurementvalue to compensate for the lag or delay associated with the filteringbeing performed by the signal processing system (tasks 1106, 1108). Inthis regard, the adaptive filtering module 714 calculates or otherwisedetermines a modified rate of change for the filtered measurement signalbased at least in part on the first derivative metric associated withthe current filtered measurement value and modifies the current filteredmeasurement value using the modified rate of change when the signalnoise estimate (n_(e)) is less than an upper noise threshold value. Forexample, in one embodiment, the adaptive filtering module 714 modifiesthe current filtered measurement value using the signal noise estimate(n_(e)) when the signal noise estimate (n_(e)) is less than or equal tothe upper noise threshold value of 3 and greater than 1 (e.g., when1<n_(e)≦3) using equation i_(adj)=i_(sig)+5(roc×(1.5−0.5n_(e))), whereroc is the modified rate of change having a value between −1 and 1,i_(sig) is the current filtered measurement value, and i_(adj) is theadjusted filtered measurement value. Alternatively, when the signalnoise estimate (n_(e)) is less than 1, the adaptive filtering module 714determines the adjusted filtered measurement value using equationi_(adj)=i_(sig)+5×roc. In exemplary embodiments, when the signal noiseestimate (n_(e)) is greater than the upper noise threshold value, theadaptive filtering module 714 does not adjust or otherwise modify thecurrent filtered measurement value based on the modified rate of change(e.g., i_(adj)=i_(sig)).

In exemplary embodiments, the modified rate of change is determined as aweighted sum of the first derivative associated with the instantfiltered measurement value (e.g., the difference between the instantfiltered measurement value and the immediately preceding filteredmeasurement value in buffer 708) and the first derivative metricassociated with the filtered measurement values in the buffer 708 (e.g.,the first derivative metric from the signal analysis module 710 that wasdetermined at 902). In exemplary embodiments, when the magnitude of themodified rate of change is greater than a maximum value, the modifiedrate of change is set to be equal to zero, such that i_(adj)=i_(sig).When the magnitude of the modified rate of change is less than themaximum value, the adaptive filtering module 714 clips the modified rateof change so that its magnitude is less than or equal to a fraction (orpercentage) of the maximum value. For example, in one embodiment, theadaptive filtering module 714 clips the modified rate of change tovalues between −1 mg/dL/min and 1 mg/dL/min.

In response to detecting a dropout condition, in exemplary embodiments,the adaptive filtering process 1100 continues by adjusting or otherwisemodifying the filtered measurement value and/or the process variancemetric (task 1110). In this regard, when the dropout condition isdetected, the adaptive filtering module 714 adds an offset to thefiltered measurement value to mitigate or otherwise compensate for thedropout condition. Additionally, the adaptive filtering module 714determines an alternative process variance metric value in response tothe dropout condition and substitutes the alternative process variancemetric value for the calculated process variance metric (Q) when thealternative process variance metric value is less than the calculatedprocess variance metric. In exemplary embodiments, the adaptivefiltering module 714 implements a counter that tracks the number offiltered measurement values since the dropout condition to progressivelydecrease the offset and progressively increase the alternative processvariance metric value to phase out the dropout modifications. Forexample, when the dropout condition is initially identified by thedropout detection module 712, the adaptive filtering module 714 mayincrement the counter from zero to one. Thereafter, the adaptivefiltering module 714 calculates or otherwise determines the dropoutoffset and the alternative process variance metric value based on thevalue of the counter, adds the dropout offset to the filteredmeasurement value to obtain a dropout compensated filtered measurementvalue, and substitutes the alternative process variance metric value forthe calculated process variance metric value when the alternativeprocess variance metric value is less than the calculated processvariance metric value.

In exemplary embodiments, the equations for calculating the dropoutoffset and the alternative process variance metric value are configuredsuch that the dropout offset decreases and the alternative processvariance metric value increases as the value of the counter increases.In this manner, if the dropout condition is still identified onsubsequent samples, the added dropout offset will be progressivelyreduced for each sample towards zero while the alternative processvariance metric value progressively increases for each sample byincrementing the value of the counter. Once a dropout condition is nolonger identified, the adaptive filtering module 714 may clear orotherwise reset the dropout counter to zero. Additionally, in someembodiments, the adaptive filtering module 714 may automatically stopadjusting the filtered measurement value and/or the process variancemetric when the value of dropout counter is greater than an upperthreshold number of measurement samples. It should be noted that in someembodiments, where the infusion device 502 and/or pump control system520 are configured to present a graphical representation of the user'ssensed glucose value on a display (e.g., user interface element 540),using a dropout compensated filtered measurement for determining theoutput measurement (which is subsequently converted to the sensedglucose value using a calibration factor) improves the user experienceby eliminating or otherwise reducing the visibility of dropouts in thedisplayed sensed glucose value that could otherwise alarm the user orcause the user to take unnecessary action(s).

Still referring to FIG. 11, in exemplary embodiments, the adaptivefiltering process 1100 continues by calculating or otherwise determiningan intermediate error estimate based on the output error estimate fromthe preceding iteration of the adaptive filtering process 1100 and theprocess variance metric (task 1112). In exemplary embodiments, theadaptive filtering module 714 determines the intermediate error estimate(p_(i)) by adding the process variance metric to the output errorestimate (p_(out)[n−1]) from the preceding iteration of the adaptivefiltering process 1100 that was fed back to the adaptive filteringmodule 714 (e.g., p=p_(out)[n−1]+Q). The adaptive filtering process 1100continues by calculating or otherwise determining a gain value based onthe intermediate error estimate and a measurement error value (task1114). In this regard, the measurement error value represents noiseattributable to electromagnetic interference, movement of the sensingelement 604, or interference caused by other non-glucose molecules inthe interstitial fluid. In exemplary embodiments, the adaptive filteringmodule 714 determines a Kalman gain value (k) using the equationk=p_(i)/(p_(i)+r) r represents the measurement error value. In oneembodiment, the measurement error value is a fixed value equal to one.

After determining the filter gain value, the adaptive filtering process1100 continues by calculating or otherwise determining the outputmeasurement value based on the current filtered measurement value, thefilter gain value, and the previous output measurement value (task1116). In exemplary embodiments, the adaptive filtering module 714determines the output measurement value according to the equationi_(out)=i_(out)[n−1]+k(i_(sig)−i_(out)[n−1]), where i_(out) is theoutput measurement value, i_(out) [n−1] represents the preceding outputmeasurement value, and i_(sig) represents the current (or most recent)filtered measurement value input to the adaptive filtering module 714 asmodified pursuant to any adjustments for filtering delay (task 1106)and/or dropout conditions (task 1110) that were made to the inputfiltered measurement value as described above. The adaptive filteringprocess 1100 also calculates or otherwise determines an updated outputerror estimate based on the filter gain value and the intermediate errorestimate (task 1118). In exemplary embodiments, the adaptive filteringmodule 714 determines the output error estimate (p_(out)) according tothe equation using the equation p_(out)=p_(i)(1−k).

Referring again to FIGS. 5-7, the output filtered measurement value andthe output error estimate determined by the adaptive filtering module714 are fed back or otherwise maintained by the adaptive filteringmodule 714 for use in determining the subsequent output filteredmeasurement value based on the preceding output filtered measurementvalue and the preceding output error estimate. In exemplary embodimentsdescribed herein, the output filtered measurement value is provided tothe calibration application 612 for calculating or otherwise determininga calibration factor for converting the uncalibrated output filteredmeasurement value to a sensor glucose value. Additionally, the outputfiltered measurement value is also provided to the health monitoringapplication 614 for determining when a maintenance condition exists withrespect to the sensing element 604. In some embodiments, theuncalibrated output filtered measurement value is also provided to thepump control system 520 and/or a user interface 540 (e.g., via theoutput interface 606) for determining dosage commands for operating themotor 507 of the infusion device 502, displaying or otherwise presentinga graphical representation or other indication of the sensed glucoselevel in the body 501 of the user, and/or the like.

FIG. 12 depicts an exemplary calibration process 1200 for calibrating asensing arrangement 504, 600 for converting the uncalibrated measurementvalues determined based on electrical signals output by the sensingelement 604 into corresponding calibrated measurement values. Thevarious tasks performed in connection with the calibration process 1200may be performed by hardware, firmware, software executed by processingcircuitry, or any combination thereof. For illustrative purposes, thefollowing description refers to elements mentioned above in connectionwith FIGS. 1-7. In practice, portions of the calibration process 1200may be performed by different elements of the sensing arrangement 504,600 and/or the control system 500. That said, in exemplary embodimentsdescribed herein, the calibration process 1200 is performed by thecalibration application 612 implemented by the control module 602. Itshould be appreciated that the calibration process 1200 may include anynumber of additional or alternative tasks, the tasks need not beperformed in the illustrated order and/or the tasks may be performedconcurrently, and/or the calibration process 1200 may be incorporatedinto a more comprehensive procedure or process having additionalfunctionality not described in detail herein. Moreover, one or more ofthe tasks shown and described in the context of FIG. 12 could be omittedfrom a practical embodiment of the calibration process 1200 as long asthe intended overall functionality remains intact.

In exemplary embodiments, the calibration process 1200 begins byanalyzing the uncalibrated measurements during an initialization periodto detect or otherwise identify when the uncalibrated measurements arestable and maintains the initialization period while the uncalibratedmeasurements are unstable (tasks 1202, 1204). In this regard, theinitialization period imposed by the calibration process 1200 delayscalibration of the sensing arrangement 504, 600 until the electricalsignals generated by the sensing element 604 stabilize. In exemplaryembodiments, the calibration application 612 determines the uncalibratedmeasurements are stable when a number of consecutive measurements outputby the data management application 610 are within a predetermined rangeof values and a difference or change between successive measurements isless than a threshold amount. For example, in one embodiment, thecalibration application 612 determines the uncalibrated measurements arestable when three consecutive measurements output by the data managementapplication 610 are within a range of measurement values and thedifferences between successive measurements of the three consecutivemeasurements are less than a threshold percentage of the precedingmeasurement of the pair. In this regard, the calibration application 612detects that the uncalibrated measurements are stable when the mostrecent uncalibrated measurement from the data management application 610is within the threshold percentage of the preceding measurement, whichis within the threshold percentage of the next preceding measurement,and each of those three most recent uncalibrated measurements from thedata management application 610 is within a particular range of values.Once the uncalibrated measurements are stable, the calibrationapplication 612 may indicate to the pump control system 520 that theuser may be notified that the sensing arrangement 504, 600 is ready forcalibration. Alternatively, the calibration application 612 may generatea user notification that the sensing arrangement 504, 600 is ready forcalibration via an output user interface 606.

After confirming the uncalibrated measurements are stable, thecalibration process 1200 proceeds by receiving or otherwise obtaining anew reference measurement value and detecting or otherwise identifyingwhether an error condition exists based at least in part on the newreference measurement value (tasks 1206, 1208, 1210). In this regard,the user may manipulate the blood glucose meter 530 to obtain a newblood glucose measurement from the user's body 501 and transmit orotherwise provide the new blood glucose measurement value to thecalibration application 612 and/or sensing arrangement 504, 600. Inresponse to identifying an error condition, the calibration process 1200generates or otherwise provide a user notification that indicates a newreference measurement value needs to be obtained for calibration (task1212). In this regard, the calibration application 612 generates orotherwise provides an indication to the user via an output userinterface element 540 associated with the infusion device 502 (e.g., viapump control system 520) and/or an output interface 606 associated withthe sensing arrangement 504, 600.

In exemplary embodiments, the calibration application 612 calculates orotherwise determines one or more calibration ratios associated with thenew blood glucose measurement value and detects or otherwise identifieswhether the calibration ratio(s) are indicative of an error condition(e.g., task 1208). In this regard, the calibration application 612identifies an error condition when a calibration ratio associated withthe new blood glucose measurement value is not within a range ofacceptable values. For example, the calibration application 612 maydetermine a first calibration ratio, alternatively referred to herein asa current calibration ratio, by dividing the new blood glucosemeasurement value by the sum of the most recent filtered measurementvalue from the data management application 610 and/or adaptive filteringmodule 714 and an offset value. The offset value represents the baselinecurrent for the sensing element 604 (e.g., the nominal current output bythe sensing element 604 in the absence of any measurable glucose). Thecalibration application 612 may also determine one or more predictedcalibration ratios by dividing the new blood glucose measurement valueby the sum of a predicted measurement value and the offset value. Inthis regard, the predicted measurement value represents the expectedmeasurement value from the data management application 610 and/oradaptive filtering module 714 at some point in the future that will bepaired with the new blood glucose measurement value for determining thecalibration factor. In exemplary embodiments, if any of the currentcalibration ratio or the predicted calibration ratios is outside of theallowable range of calibration factor values, the calibrationapplication 612 generates or otherwise provides a notificationindicative of a need to re-obtain a new blood glucose measurement withthe blood glucose meter 530 as described above.

In exemplary embodiments, the calibration application 612 also comparesthe current calibration ratio to the calibration factor currently beingutilized by the pump control system 520 and/or sensing arrangement 504,600 and detects or otherwise identifies an error condition when thedifference between the current calibration ratio and the currentcalibration factor is greater than a threshold amount (e.g., apercentage of the current calibration factor). Similarly, thecalibration application 612 also compares the current calibration ratioto the preceding calibration ratio (e.g., the preceding reference bloodglucose measurement value from the blood glucose meter 530 divided byits paired uncalibrated filtered measurement value) and detects orotherwise identifies an error condition when the difference between thecurrent calibration ratio and the preceding calibration ratio is greaterthan a threshold amount (e.g., a percentage of the preceding calibrationratio).

In exemplary embodiments, the calibration application 612 alsocalculates or otherwise determines a difference between the new bloodglucose measurement value and the most recent sensed measurement valuedetermined based on the most recent filtered measurement value and thecurrent calibration factor and detects or otherwise identifies an errorcondition when the difference exceeds a threshold value (e.g., task1210). For example, in one embodiment, the calibration application 612detects an error condition when the difference between the new bloodglucose measurement value and the most recent sensed measurement valueis greater than a threshold glucose concentration value.

After confirming an error condition does not exist, the calibrationprocess 1200 continues by identifying or otherwise determining whether acorresponding unfiltered measurement value for pairing with the newblood glucose measurement value is available (task 1214). In thisregard, in one or more exemplary embodiments, the calibrationapplication 612 implements a timer or another similar feature and waitsfor at least a threshold duration of time before selecting the nextfiltered measurement value from the data management application 610and/or the adaptive filtering module 714 for pairing with a referenceblood glucose measurement value. For example, in one embodiment, thecalibration application 612 waits for at least ten minutes from the timeof the reference blood glucose measurement value before selecting thenext filtered measurement value from the data management application 610and/or the adaptive filtering module 714 for pairing with the referenceblood glucose measurement value. After the threshold amount of time haselapsed, the calibration process 1200 pairs the next valid unfilteredmeasurement value with the reference blood glucose measurement value andcalculates or otherwise determines a new (or updated) calibration factorfor converting the uncalibrated measurement values into calibratedvalues based on the new reference blood glucose measurement value andits paired uncalibrated measurement value (task 1216), as described ingreater detail below in the context of the calibration factordetermination process 1300 of FIG. 13.

In exemplary embodiments, when an unfiltered measurement value is notyet available for pairing, the calibration process 1200 dynamicallyadjusts the calibration factor towards an expected calibration factorindicated by the relationship between the new reference blood glucosemeasurement value and the uncalibrated measurement value(s) receivedafter the new reference blood glucose measurement value was obtained(task 1218). In this regard, during the period where the calibrationapplication 612 is waiting for an unfiltered measurement value forpairing, the calibration application 612 may determine an adjustedcalibration factor that is used by the sensing arrangement 504, 600and/or the pump control system 520 to convert any filtered measurementvalues output by the data management application 610 and/or the adaptivefiltering module 714 during that period into a sensed measurement value(e.g., for presenting the sensed glucose value on a display 540 and/ordetermining delivery commands during the ten minute waiting period). Inone embodiment, the calibration application 612 temporarily pairs thenew reference blood glucose measurement value with an updated filteredmeasurement value output by the data management application 610 and/orthe adaptive filtering module 714 and calculates an intermediatecalibration factor based on the temporary pairing and stored pairings ofreference blood glucose measurement values and filtered measurementvalues in a similar manner as described in greater detail below (e.g.,task 1310). In this regard, the intermediate calibration factorrepresents an expected calibration factor if the new reference bloodglucose measurement value were to be paired with an uncalibratedmeasurement value equal to the current output from the data managementapplication 610 and/or the signal analysis module 710.

In exemplary embodiment, the calibration application 612 determines anadjusted calibration factor as a weighted sum of the intermediatecalibration factor and the calibration factor currently being utilizedby the sensing arrangement 504, 600 and/or the pump control system 520.For example, in one embodiment, the calibration application 612 weightsthe intermediate calibration factor with the current calibration factorby multiplying the intermediate calibration factor by 70%, multiplyingthe current calibration factor by 30%, and adding the two products toobtain the adjusted calibration factor. In exemplary embodiments, thecalibration application 612 dynamically adjusts the calibration factortowards the expected calibration factor by substituting the adjustedcalibration factor for use in lieu of the current calibration factorbased on one or more derivative metrics associated with filteredmeasurement values from the data management application 610 and/or theadaptive filtering module 714. In this regard, if the product of thefirst derivative metric associated with the current (or most recent)filtered measurement value in the buffer 708 and the current calibrationfactor is greater than one, the calibration application 612 selects thelesser of the current calibration factor and the adjusted calibrationfactor for use in determining a sensed glucose value based on thecurrent uncalibrated measurement value from the data managementapplication 610 and/or the adaptive filtering module 714. Conversely, ifthe product of the first derivative metric associated with the current(or most recent) filtered measurement value in the buffer 708 and thecurrent calibration factor is less than one, the calibration application612 selects the greater of the current calibration factor and theadjusted calibration factor for use in determining a sensed glucosevalue based on the current uncalibrated measurement value from the datamanagement application 610 and/or the adaptive filtering module 714.

FIG. 13 depicts an exemplary calibration factor determination process1300 for determining a calibration factor used to convert uncalibratedmeasurement values subsequently output by a sensing arrangement 504, 600into corresponding calibrated measurement values. In one or moreexemplary embodiments, the calibration factor determination process 1300is performed at task 1216 of the calibration process 1200 of FIG. 12.The various tasks performed in connection with the calibration factordetermination process 1300 may be performed by hardware, firmware,software executed by processing circuitry, or any combination thereof.For illustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-7. In practice, portions ofthe calibration factor determination process 1300 may be performed bydifferent elements of the sensing arrangement 504, 600 and/or thecontrol system 500. That said, in exemplary embodiments describedherein, the calibration factor determination process 1300 is performedby the calibration application 612 implemented by the control module602. It should be appreciated that the calibration factor determinationprocess 1300 may include any number of additional or alternative tasks,the tasks need not be performed in the illustrated order and/or thetasks may be performed concurrently, and/or the calibration factordetermination process 1300 may be incorporated into a more comprehensiveprocedure or process having additional functionality not described indetail herein. Moreover, one or more of the tasks shown and described inthe context of FIG. 13 could be omitted from a practical embodiment ofthe calibration factor determination process 1300 as long as theintended overall functionality remains intact.

In exemplary embodiments, the calibration factor determination process1300 begins by obtaining paired reference blood glucose measurementvalues and corresponding uncalibrated filtered current measurementvalues, calculating or otherwise determining one or more weightingfactors for the respective pairs of blood glucose and uncalibratedmeasurement values, and calculating or otherwise determining a raw (orunadjusted) calibration factor based on the relationships between thepaired measurement values and the respective weighting factorsassociated therewith (tasks 1302, 1304, 1306). In this regard, inexemplary embodiments, the calibration application 612 implements orotherwise provides a calibration buffer (e.g., in memory 608) thatstores or otherwise maintains a number of reference blood glucosemeasurement values previously received from the blood glucose meter 530along with the respective paired uncalibrated filtered currentmeasurement value (i_(out)) from the data management application 610and/or the adaptive filtering module 714 that corresponds to thatrespective blood glucose measurement value (e.g., the i_(out) value thatwas output between 10 to 15 minutes after the respective blood glucosemeasurement value was obtained). In one exemplary embodiment, thecalibration application 612 updates the calibration buffer to maintainthe new reference blood glucose measurement value and its paireduncalibrated filtered current measurement value along with the threepreceding pairs of reference blood glucose measurement values andcorresponding uncalibrated filtered current measurement values.

In exemplary embodiments, the calibration application 612 determines theraw calibration factor as a ratio of a first weighted sum of therespective products of each respective measurement pair with respect toa second weighted sum of the respective square of each respectiveuncalibrated measurement value. In this regard, in one or moreembodiments, the first weighted sum is a sum of the respective productsof each respective reference measurement value, its paired uncalibratedfiltered current measurement value, and weighting factors associatedwith that respective blood glucose-current measurement pair, while thesecond weighted sum is a sum of the respective products of the square ofeach respective uncalibrated filtered current measurement value and theweighting factors associated with that measurement's respective bloodglucose-current measurement pair. For example, the raw calibrationfactor (CF_(R)) may be governed by the equation:

${{CF}_{R} = \frac{\sum{\alpha_{i}\beta_{i}i_{{out}_{i}}{BG}_{i}}}{\sum{\alpha_{i}\beta_{i}i_{{out}_{i}}^{2}}}},$

where i_(out) _(i) represents the uncalibrated filtered currentmeasurement value of a respective blood glucose-current measurementpair, BG_(i) represents the reference blood glucose measurement value ofthat respective blood glucose-current measurement pair, α_(i) representsa first weighting factor associated with that respective bloodglucose-current measurement pair, and β_(i) represents a secondweighting factor associated with that respective blood glucose-currentmeasurement pair. The α weighting factors generally represent FIR filtercoefficients for the calibration ratios associated with the respectiveblood glucose-current measurement pairs, while the β weighting factorscompensate for the nonlinearities in the response of the sensing element604 (e.g., nonlinearities in the relationship between the output signalsgenerated by the sensing element 604 and the actual fluid glucoselevel).

In one or more embodiments, the calibration application 612 determinesthe first weighting factor (α_(i)) as a fixed value based on therelative age of the respective blood glucose-current measurement pair.In alternative embodiments, the calibration t/application 612 maydetermine the first weighting factors using the equationα_(i)=e^(−Δt/7), where Δt represents the amount of time between arespective reference blood glucose measurement value and the precedingreference blood glucose measurement value. In such embodiments, thecalibration buffer may also store or otherwise maintain, in associationwith each respective blood glucose-current measurement pair, the timeassociated with the respective blood glucose-current measurement pair.In this regard, fixed values may be utilized to emulate exponentialdecay in lieu of deterministically calculating the first weightingfactor using an exponential decay function. In one or more embodiments,the calibration application 612 determines the second weighting factor(β_(i)) as a function of the reference blood glucose measurement valueof the respective blood glucose-current measurement pair. For example,the calibration application 612 may calculate the second weightingfactor using the equation β_(i)=g₁BG_(i) ^(−g) ² −g₃, where g₁represents a linear scaling factor for the glucose response of thesensing element 604, g₂ represents an exponential scaling factor for theglucose response of the sensing element 604, and g₃ represents an offsetvalue for the glucose response of the sensing element 604. Afterdetermining the weighting factors for each of the respective bloodglucose-current measurement pairs in the calibration buffer, thecalibration application 612 then calculates the raw calibration factor(CF_(R)) as the ratio of the weighted sums described above.

Still referring to FIG. 13, in exemplary embodiments, the calibrationfactor determination process 1300 continues by identifying or otherwisedetermining whether the raw calibration factor is within an acceptablerange of values (task 1308). When the value of the raw calibrationfactor is acceptable, the calibration factor determination process 1300continues by obtaining or otherwise identifying an expected calibrationfactor for the sensing arrangement and a weighting factor for theexpected calibration factor, and determining an adjusted calibrationfactor based on the raw calibration factor, the expected calibrationfactor, and the weighting for the expected calibration factor (1310,1312, 1314). In this manner, the calibration factor determinationprocess 1300 effectively normalizes the raw calibration factor toaccount for potential inaccuracies in the reference blood glucosemeasurement value(s) and/or the uncalibrated filtered currentmeasurement value(s) that could be caused by normal variations insensitivities or other transient events. The expected calibration factorrepresents the anticipated or likely calibration ratio between areference blood glucose measurement value and its correspondinguncalibrated measurement value from the data management application 610and/or the adaptive filtering module 714. In one or more embodiments,the expected calibration factor may be a fixed value that is empiricallydetermined by testing multiple sensing elements 604 and/or sensingarrangements 600 and determining a nominal (or average) calibrationratio across the tested sensing elements 604 and/or sensing arrangements600. For example, in one embodiment, the expected calibration factor isa fixed value equal to 5 mg/dL/nA. In other embodiments, the calibrationapplication 612 may dynamically determine the expected calibrationfactor as a historical average of the raw calibration factor valuesdetermined during previous iterations of the calibration factordetermination process 1300 with preceding blood glucose-currentmeasurement pairs. In exemplary embodiments, the range of acceptablevalues is chosen to encompass the expected calibration factor. Forexample, in one embodiment, the acceptable range of values is chosen tobe from the expected calibration factor minus a threshold amount to theexpected calibration factor plus the threshold amount, such that any rawcalibration factor within that range (e.g., the expected calibrationfactor value plus/minus the threshold amount) would be adjusted.

In exemplary embodiments, the calibration application 612 determines theadjusted calibration factor (CF_(ADJ)) as a weighted sum of the expectedcalibration factor (CF_(EXP)) and the raw calibration factor using theequation CF_(ADJ)=γCF_(EXP)+(1−γ)CF_(R), where γ represents theweighting factor obtained for the expected calibration factor. In oneembodiment, the weighting factor for the expected calibration factor hasa fixed value between zero and one (e.g., a fixed percentage). In otherembodiments, the calibration application 612 may dynamically determinethe expected calibration factor weighting factor based on one or morefactors. For example, if the expected calibration factor is determinedbased on a number of raw calibration factor values determined duringprevious iterations of the calibration factor determination process1300, the expected calibration factor weighting factor may increase asthe number of raw calibration factor values used to determine theexpected calibration factor, and vice versa, thereby reflecting therelative confidence or reliability associated with the expectedcalibration factor. After the calibration application 612 determines theadjusted calibration factor, the calibration application 612 maytransmit or otherwise provide the adjusted calibration factor to thepump control system 520 via the communications interface 606 for use bythe pump control system 520 to convert filtered current measurementsprovided by the data management application 610 and/or the adaptivefiltering module 714 into sensed glucose measurement values (e.g.,SG=CF_(ADJ)×i_(out)) and subsequently generating delivery commandsand/or graphical user interface displays using the sensed glucosemeasurement values. Additionally, the adjusted calibration factor may beprovided to the data management application 610 and/or the signalanalysis module 710 for determining the frequency and noise metrics forthe filtered measurement signal as described above in the context ofFIG. 9.

Still referring to FIG. 13, in exemplary embodiments, when thecalibration factor determination process 1300 determines that the rawcalibration factor is not within an acceptable range of values, thecalibration factor determination process 1300 proceeds by identifying orotherwise determining whether the blood glucose-current measurement pairwas obtained during a startup period where the sensitivity of thesensing arrangement is abnormal or otherwise deviates from its likelylong-term sensitivity (task 1316). In this regard, in some situations,the sensitivity of the sensing element 604 may initially be abnormallyhigh or abnormally low upon initialization of the sensing element 604before settling or otherwise converging towards a sensitivity that wouldresult in a raw calibration factor value within the acceptable range ofvalues for the raw calibration factor. In exemplary embodiments, whenthe calibration factor determination process 1300 determines thecalibration attempt was performed during a startup period for thesensing arrangement, the calibration factor determination process 1300continues by dynamically adjusting the calibration factor towards anexpected calibration factor (task 1320). As described in greater detailbelow in the context of FIG. 14, the calibration application 612determines an adjusted calibration factor based at least in part on theraw calibration factor in a manner that results in the adjustedcalibration factor converging towards an expected calibration factorvalue or an acceptable range of values for the calibration factor. Inthis regard, as the amount of time elapsed since the calibration attemptincreases, the difference between the adjusted calibration factor andthe expected calibration factor (or alternatively, the acceptable rangeof values for the calibration factor) decreases.

Referring now to FIG. 14, in one or more embodiments, the calibrationapplication 612 performs a dynamic calibration factor adjustment process1400 to dynamically adjust the calibration factor towards an expectedcalibration factor value (or a range of values) when the calibrationattempt is performed during a startup period for the sensing element 604of the sensing arrangement 504, 600. The dynamic adjustment process 1400begins by calculating or otherwise determining an adjusted calibrationfactor based on the raw calibration factor and the amount of time thathas elapsed since the calibration attempt (task 1402). In oneembodiment, the calibration application 612 calculates the adjustedcalibration factor using the equation CF_(ADJ)=CF_(R)+t(p₁CF_(R)+p₂),where t is the amount of time elapsed since the time associated with theraw calibration factor and p₁ and p₂ are scalar values configured tolinearly adjust the adjusted calibration factor towards the expectedcalibration factor.

The illustrated process 1400 continues by identifying or otherwisedetermining whether the calibration factor is within an acceptable rangeof values (task 1404) and automatically terminating the dynamicadjustment process 1400 when the calibration factor is within theacceptable range of values. In this regard, the calibration application612 ceases adjusting the calibration factor once the amount of time thathas elapsed since the calibration attempt has resulted in an adjustedcalibration factor that is sufficiently close to the expectedcalibration factor. In one or more embodiments, the calibrationapplication 612 stops adjusting the calibration factor when the adjustedcalibration factor is within a range of acceptable calibration factorvalues, where the range encompasses the expected calibration factor. Insome embodiments, the calibration application 612 also confirms that theraw calibration factor does not exceed a maximum calibration factorvalue or fall below a minimum calibration factor value. For example, inone embodiment, the calibration application 612 only allows thecalibration factor to be adjusted when the raw calibration factor iswithin the range of the minimum calibration factor value to the lowerend of the range of acceptable calibration factor values or within therange of the upper end of the range of acceptable calibration factorvalues to the maximum calibration factor value. The dynamic adjustmentprocess 1400 exits and provides indication that recalibration isrequired (e.g., task 1322) when the raw calibration factor is less thanthe minimum calibration factor value or greater than the maximumcalibration factor value.

Still referring to FIG. 14, the dynamic adjustment process 1400continues by verifying or otherwise confirming that the amount of timethat has elapsed since the sensing element 604 of the sensingarrangement 504, 600 was initialized is less than the startup period forthe sensing element 604 (task 1406). In this regard, the calibrationapplication 612 or another component of the control module 602 mayimplement a timer or another similar feature to track or otherwisemonitor the amount of time elapsed since the sensing element 604 wasinstalled, replaced, or otherwise initialized, and terminate the dynamicadjustment process 1400 when the calibration application 612 detectsthat the elapsed time is greater than a threshold startup time period.For example, in one embodiment, the threshold startup time period is setto ten hours, such that the calibration application 612 automaticallyterminates the dynamic adjustment process 1400 when ten hours haveelapsed since the sensing element 604 was first installed in the sensingarrangement 504, 600.

The illustrated process 1400 continues by identifying or otherwisedetermining whether the sensed glucose value corresponding to the mostrecent uncalibrated filtered current measurement value and the adjustedcalibration factor value is less than a threshold amount (task 1408). Inthis regard, the calibration application 612 multiplies the mostrecently obtained uncalibrated filtered current measurement value fromthe data management application 610 and/or the adaptive filtering module714 by the current adjusted calibration factor to determine the currentsensed glucose value (e.g., SG=CF_(ADJ)×i_(out)). The dynamic adjustmentprocess 1400 automatically suspends adjusting the calibration factorwhen the sensed glucose value is less than a threshold glucoseconcentration and maintains the calibration factor at its current value(task 1410). For example, in one embodiment, the monitoring application614 automatically maintains the current calibration factor value whenthe current sensed glucose value is less than 60 mg/dL. In this regard,the dynamic adjustment process 1400 does not adjust the calibrationfactor when the sensed glucose value is indicative of a potentialhypoglycemic condition of the user. As illustrated in FIG. 14, thedynamic adjustment process 1400 may repeat until the adjustedcalibration factor is within an acceptable range of calibration factorvalues or the sensor startup period has elapsed (e.g., a thresholdnumber of hours from initialization of the sensing element 604).

Referring again to FIG. 13, in the illustrated embodiment, when thecalibration factor determination process 1300 determines that the rawcalibration factor is not within an acceptable range of values and thatthe sensor startup period has elapsed, the calibration factordetermination process 1300 identifies or otherwise determines whetherthe blood glucose-current measurement pair was obtained at a point intime when the output from the sensing element was abnormally low (task1318), and if so, the calibration factor determination process 1300dynamically adjusts the calibration factor towards an expectedcalibration factor (task 1320). In exemplary embodiments, thecalibration application 612 detects that the uncalibrated filteredcurrent measurement value paired with the new blood glucose referencemeasurement value is abnormally low when the calibration ratio for thenew blood glucose reference measurement value and its paireduncalibrated filtered current measurement value is greater than thecalibration factor currently being utilized (e.g., the calibrationfactor determined by the calibration factor determination process 1300on the preceding calibration attempt) by at least a threshold amount,the uncalibrated filtered current measurement value is less than athreshold current, and the current calibration factor is less than athreshold calibration value. For example, in one embodiment, thecalibration application 612 detects the low current calibrationcondition when the calibration ratio is more than a threshold percentagegreater than the current calibration factor, the uncalibrated filteredcurrent measurement value is less than threshold current value, and thecurrent calibration factor is less than a threshold calibration factor.Depending on the embodiment, after detecting the low current calibrationcondition, the calibration application 612 may fail to update thecalibration buffer to include the current blood glucose-currentmeasurement pair (e.g., by failing to evict the oldest bloodglucose-current measurement pair from the buffer), or alternatively, thecalibration application 612 may flag the current blood glucose-currentmeasurement pair for eviction/replacement on the next calibrationattempt if the calibration ratio associated with the next bloodglucose-current measurement pair is less than the calibration ratioassociated with the current blood glucose-current measurement pair.

In exemplary embodiments, after detecting the low current calibrationcondition, the calibration application 612 utilizes the raw calibrationfactor until identifying that the sensing element 604 has recovered fromthe low current condition, and thereafter, automatically beginsdynamically adjusting the raw calibration factor for the new bloodglucose-current measurement pair in response to detecting the recovery.In one embodiment, the calibration application 612 detects recovery fromthe low current condition when the uncalibrated measurement value fromthe data management application 610 and/or the adaptive filtering module714 exceeds the uncalibrated measurement value of the bloodglucose-current measurement pair by at least a threshold percentage ofthe paired uncalibrated measurement value. In a similar manner asdescribed above, the calibration application 612 may linearly adjust thecalibration factor towards the expected calibration factor (e.g., 5mg/dL/nA) as the amount of time elapsed increases until the adjustedcalibration factor is less than a threshold amount or the adjustedcalibration factor is within a threshold percentage of the precedingcalibration factor. Additionally, in one or more embodiments, thecalibration application 612 stops adjusting the calibration factor inresponse to a new (or subsequent) calibration attempt having acalibration ratio that is not more than the threshold percentage greaterthan the current calibration factor. It should be noted that in suchembodiments, the calibration factor determination process 1300 will beperformed using the subsequent blood glucose-current measurement pair todetermine a new calibration factor for use in converting subsequentcurrent measurements into sensed glucose values.

Referring again to FIG. 13, when the calibration factor determinationprocess 1300 determines that the raw calibration factor is not within anacceptable range of values, and that that deviation is not attributableto the sensor startup period or an abnormally low current measurement,the calibration factor determination process 1300 automaticallygenerates or otherwise provides a notification to the user thatindicates recalibration is required (task 1322). For example, thecalibration application 612 may transmit or otherwise provide anotification to the pump control system 520 that indicates thecalibration was unsuccessful, and in response, the pump control system520 may generate or otherwise provide one or more auditory and/or visualnotifications to the user via one or more output user interfaceelement(s) 540 that a new blood glucose measurement value is required ina similar manner as described above (e.g., task 1212).

FIG. 15 depicts an exemplary sensor monitoring process 1500 foridentifying when a maintenance condition exists for a sensing element.In this regard, a maintenance condition indicates that the sensingelement should be replaced or that some other maintenance of the sensingelement should otherwise be performed (e.g., inspecting electricalconnectivity to/from the sensing element, ensuring the sensing elementis properly inserted and/or fitted in a housing of a sensing device, orthe like). The various tasks performed in connection with the sensormonitoring process 1500 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-7. In practice, portions ofthe sensor monitoring process 1500 may be performed by differentelements of the sensing arrangement 504, 600 and/or the control system500. That said, in exemplary embodiments described herein, the sensormonitoring process 1500 is performed by the health monitoringapplication 614 implemented by the control module 602 to detect when thesensing element 604 should be replaced. It should be appreciated thatthe sensor monitoring process 1500 may include any number of additionalor alternative tasks, the tasks need not be performed in the illustratedorder and/or the tasks may be performed concurrently, and/or the sensormonitoring process 1500 may be incorporated into a more comprehensiveprocedure or process having additional functionality not described indetail herein. Moreover, one or more of the tasks shown and described inthe context of FIG. 15 could be omitted from a practical embodiment ofthe sensor monitoring process 1500 as long as the intended overallfunctionality remains intact.

The illustrated sensor monitoring process 1500 calculates or otherwisedetermines an average filtered current measurement value over theduration of a particular monitoring window (task 1502). In this regard,the monitoring application 614 receives or otherwise obtains theuncalibrated filtered current measurements output by the data managementapplication 610 and/or the adaptive filtering module 714 and averagesany filtered current measurements occurring within a particularmonitoring window of time. For example, in one embodiment, themonitoring application 614 implements a monitoring period of two hours,where the monitoring application 614 calculates the mean currentmeasurement for a respective monitoring period by averaging the filteredcurrent measurements output by the data management application 610and/or the adaptive filtering module 714 over a consecutive two hourtime period (e.g., 24 consecutive samples when filtered measurements areobtained every 5 minutes).

In exemplary embodiments, the sensor monitoring process 1500 alsocalculates or otherwise determines a number of filtered currentmeasurement values during the particular monitoring window that have anassociated noise metric greater than a threshold value (task 1504). Inthis regard, the monitoring application 614 implements a counter oranother similar feature to track the number of filtered currentmeasurements having an associated noise metric (e.g., the estimatedsignal noise metric determined by the signal analysis module 710) thatexceeds a threshold value. In one embodiment where the signal analysismodule 710 determines an estimated signal noise metric by scaling thesecond derivative metric by the calibration factor and clips the noisemetric to an upper limit as described above, the monitoring application614 identifies a high noise measurement when the noise metric associatedwith a filtered measurement is greater than a fraction of the upperlimit (e.g., a percentage or fraction of the upper limit) and incrementsthe counter associated with the high noise measurements. For example, ifthe signal analysis module 710 clips the estimated signal noise to amaximum value, the monitoring application 614 may identify a high noisemeasurement when the noise metric associated with a filtered measurementis greater than eighty percent of the maximum value. It should be notedthat in some embodiments, an adjusted calibration factor may be usedwhen determining the estimated signal noise metric, that is, the dynamicadjustment process 1400 may be performed concurrently to the sensormonitoring process 1500.

After determining the average measurement value over a monitoring windowand a number of high noise measurements during that monitoring window,the sensor monitoring process 1500 continues by calculating or otherwisedetermining a sensor reliability metric based on relationship betweenthe average measurement value and the number of high noise measurements(task 1506). In exemplary embodiments, the monitoring application 614determines a sensor reliability metric as a ratio of the averagemeasurement value to the number of high noise measurements, for example,by dividing the average current measurement value for the monitoringwindow by the number of high noise measurements for the monitoringwindow (plus one, if desired, to prevent divide by zero). In thisregard, the sensor reliability metric calculation accounts for theanticipated relationship between the measurement signal level and themagnitude of the fluctuations in the measurement signal (e.g., largersignal fluctuations at higher signal levels are more likely to lead tomore measurements classified as high noise and vice versa).

After determining a sensor reliability metric, the sensor monitoringprocess 1500 continues by determining whether the sensor reliabilitymetric is greater than a maintenance threshold that is indicative of ahealthy sensing element (task 1508). In this regard, a higher value forthe sensor reliability metric indicates a lower number of high noisemeasurements relative to the measurement signal level over themonitoring period, while a lower value for the sensor reliability metricindicates a higher number of high noise measurements relative to themeasurement signal level over the monitoring period. Thus, when thesensor reliability metric is less than the maintenance threshold, thesensor monitoring process 1500 identifies or otherwise classifies themonitoring window as a high noise monitoring window and increments orotherwise increases a count of the number of consecutive high noisemonitoring windows and determines whether the number of consecutive highnoise monitoring windows exceeds a notification threshold amount (tasks1510, 1512). For example, in one embodiment, the notification thresholdamount is chosen to be equal to three, such that the monitoringapplication 614 determines that the user should be notified of themaintenance condition with respect to the sensing element 604 when thefiltered measurement values exhibit relatively high noise over at leastsix consecutive hours (e.g., 3 consecutive monitoring windows with a lowsensor reliability metric). In this manner, when the filteredmeasurement values exhibit relatively high noise for a consecutiveduration of time that exceeds a threshold amount of time, the sensormonitoring process 1500 determines that the maintenance notificationcriteria have been satisfied and generates or otherwise provides a usernotification that indicates maintenance condition exists with respect tothe sensing element (task 1514). For example, in one embodiment, themonitoring application 614 may operate an output user interface 606 toindicate replacement of the sensing element 604 should be performed. Inother embodiments, the monitoring application 614 may instruct the pumpcontrol system 520 to notify the user of the maintenance condition via auser interface 540 associated with the infusion device 502.Alternatively, when the sensor reliability metric for a monitoringwindow is greater than the maintenance threshold and indicative of ahealthy sensing element, the sensor monitoring process 1500 resets orotherwise reinitializes the number of consecutive high noise monitoringwindows (task 1516).

It should be noted that in one or more exemplary embodiments, the sensormonitoring process 1500 is not performed immediately upon initializationof a sensing element. For example, in one or more embodiments, themonitoring application 614 may implement a timer or another similarfeature to track the amount of time elapsed since the sensing element604 was installed or initialized in the sensing arrangement 600 andbegin performing the sensor monitoring process 1500 only after theelapsed time is greater than a threshold amount of time. In this regard,the sensor monitoring process 1500 may not performed during theinitialization period when the output signals from the sensing element604 are unstable (e.g., tasks 1202, 1204) or the sensor startup periodwhere the output signals from the sensing element 604 may otherwise beunsettled or unreliable (e.g., task 1316). For example, in oneembodiment, the monitoring application 614 does not perform the sensormonitoring process 1500 until at least 24 hours have elapsed since thesensing element 604 was installed or initialized.

For the sake of brevity, conventional techniques related to glucosesensing and/or monitoring, closed-loop glucose control, sensorcalibration and/or compensation, and other functional aspects of thesubject matter may not be described in detail herein. In addition,certain terminology may also be used in the herein for the purpose ofreference only, and thus is not intended to be limiting. For example,terms such as “first”, “second”, and other such numerical termsreferring to structures do not imply a sequence or order unless clearlyindicated by the context. The foregoing description may also refer toelements or nodes or features being “connected” or “coupled” together.As used herein, unless expressly stated otherwise, “coupled” means thatone element/node/feature is directly or indirectly joined to (ordirectly or indirectly communicates with) another element/node/feature,and not necessarily mechanically.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or embodiments described herein are not intended tolimit the scope, applicability, or configuration of the claimed subjectmatter in any way. For example, the subject matter described herein isnot limited to the infusion devices and related systems describedherein. Moreover, the foregoing detailed description will provide thoseskilled in the art with a convenient road map for implementing thedescribed embodiment or embodiments. It should be understood thatvarious changes can be made in the function and arrangement of elementswithout departing from the scope defined by the claims, which includesknown equivalents and foreseeable equivalents at the time of filing thispatent application. Accordingly, details of the exemplary embodiments orother limitations described above should not be read into the claimsabsent a clear intention to the contrary.

What is claimed is:
 1. A method of operating an infusion device operableto deliver fluid to a user, the fluid influencing a physiologicalcondition of the user, the method comprising: obtaining a filteredmeasurement indicative of the physiological condition of the user;determining a metric indicative of a characteristic of the filteredmeasurement based at least in part on one or more derivative metricsassociated with the filtered measurement; and determining an outputmeasurement indicative of the physiological condition of the user basedat least in part on the filtered measurement, the metric, and a previousoutput measurement.
 2. The method of claim 1, wherein: determining themetric comprises determining a process variance metric associated withthe filtered measurement based at least in part on a first derivativemetric associated with the filtered measurement and a second derivativemetric associated with the filtered measurement; and determining theoutput measurement comprises determining the output measurement based atleast in part on the filtered measurement, the process variance metric,and the previous output measurement.
 3. The method of claim 2, whereindetermining the process variance metric comprises: determining afrequency estimate associated with the filtered measurement based on thefirst derivative metric; determining a noise estimate associated withthe filtered measurement based on the second derivative metric; anddetermining the process variance metric as a function of the frequencyestimate and the noise estimate.
 4. The method of claim 3, wherein:determining the frequency estimate comprises scaling the firstderivative metric by a calibration factor for converting the outputmeasurement to a sensed value for the physiological condition, the firstderivative metric comprising an average of first derivative valuesassociated with the filtered measurement and one or more precedingfiltered measurements; and determining the noise estimate comprisesscaling the second derivative metric by the calibration factor, thesecond derivative metric comprising an average of second derivativevalues associated with the filtered measurement and the one or morepreceding filtered measurements.
 5. The method of claim 3, furthercomprising: determining a rate of change metric associated with thefiltered measurement based at least in part on the first derivativemetric; scaling the rate of change metric for the filtered measurementbased on the noise estimate when the noise estimate is less than athreshold value, resulting in a scaled rate of change metric; and addingthe scaled rate of change metric to the filtered measurement to obtainan adjusted filtered measurement, wherein determining the outputmeasurement comprises determining the output measurement based at leastin part on the adjusted filtered measurement, the process variancemetric, and the previous output measurement.
 6. The method of claim 1,further comprising: identifying a dropout condition based at least inpart the one or more derivative metrics associated with the filteredmeasurement; and determining an adjusted filtered measurement inresponse to identifying the dropout condition, wherein determining theoutput measurement comprises determining the output measurement based atleast in part on the adjusted filtered measurement, the metric, and theprevious output measurement.
 7. The method of claim 6, furthercomprising modifying the metric in response to identifying the dropoutcondition.
 8. The method of claim 6, wherein identifying the dropoutcondition comprises identifying the dropout condition when a firstderivative metric associated with the filtered measurement is greaterthan a first derivative dropout threshold value and a second derivativemetric associated with the filtered measurement is less than a secondderivative dropout threshold value.
 9. The method of claim 1, furthercomprising adjusting the filtered measurement to compensate for delaybased at least in part on the one or more derivative metrics associatedwith the filtered measurement, resulting in an adjusted filteredmeasurement, wherein determining the output measurement comprisesdetermining the output measurement based on the adjusted filteredmeasurement, the metric, and the previous output measurement.
 10. Themethod of claim 9, further comprising determining a noise estimateassociated with the filtered measurement based on a second derivativemetric associated with the filtered measurement, wherein adjusting thefiltered measurement comprises: scaling a rate of change metric for thefiltered measurement based on the noise estimate when the noise estimateis less than a threshold value, resulting in a scaled rate of changemetric; and adding the scaled rate of change metric to the filteredmeasurement to obtain the adjusted filtered measurement.
 11. The methodof claim 1, further comprising identifying an artifact condition when adifference between the filtered measurement and a preceding filteredmeasurement exceeds a threshold value, wherein determining the outputmeasurement comprises maintaining the previous output measurement inresponse to the artifact condition.
 12. The method of claim 1, thefiltered measurement being based on one or more output signals from asensing element, the method further comprising: determining a sensedvalue for the physiological condition of the user based on the outputmeasurement and a calibration factor associated with the sensingelement; determining a delivery command based at least in part on adifference between the sensed value and a target value for thephysiological condition of the user; and operating the infusion deviceto deliver the fluid to the user in accordance with the deliverycommand.
 13. A computer-readable medium having computer-executableinstructions stored thereon that, when executed by a control module,cause the control module to perform the method of claim
 1. 14. A devicecomprising: a sensing element to provide one or more signals influencedby a physiological condition of a user; and a control module coupled tothe sensing element to: determine a filtered measurement based on theone or more signals; determine a metric indicative of a characteristicof the filtered measurement based at least in part on one or morederivative metrics associated with the filtered measurement; anddetermine an output measurement indicative of the physiologicalcondition of the user based at least in part on the filteredmeasurement, the metric, and a previous output measurement.
 15. Thedevice of claim 14, further comprising a communications interface totransmit the output measurement to an infusion device, wherein operationof the infusion device to regulate the physiological condition of theuser is influenced by the output measurement.
 16. The device of claim14, further comprising: a first data storage element to maintain the oneor more signals; and a second data storage element to maintain thefiltered measurement.
 17. An infusion system comprising: a sensingarrangement including a sensing element to provide one or more signalscorresponding to a physiological condition in a body of a user that issensed by the sensing element, the sensing arrangement determining anoutput measurement indicative of the physiological condition based atleast in part on a filtered measurement based on the one or moresignals, a metric indicative of a characteristic of the filteredmeasurement, and a previous output measurement; and an infusion devicecomprising: a motor operable to deliver fluid to the body of the user,the fluid influencing the physiological condition; and a control systemto receive the output measurement and determine a delivery command foroperating the motor based at least in part on the output measurement.18. The infusion system of claim 17, the sensing arrangement determininga calibration factor for converting the output measurement to a sensedvalue for the physiological condition, wherein: the metric is influencedby the calibration factor; and the control system determines thedelivery command based at least in part on the sensed value.
 19. Theinfusion system of claim 18, the sensing arrangement identifying adropout condition based at least in part on one or more derivativemetrics associated with the filtered measurement and determining adropout compensated filtered measurement in response to identifying thedropout condition, the output measurement being determined based atleast in part on the dropout compensated filtered measurement, whereinthe infusion device further comprises a display to present a graphicalrepresentation of the sensed value determined based on the dropoutcompensated filtered measurement.
 20. The infusion system of claim 18,the sensing arrangement identifying an artifact condition when adifference between the filtered measurement and a preceding filteredmeasurement exceeds a threshold value and determining the outputmeasurement by maintaining the previous output measurement in responseto the artifact condition, wherein the infusion device further comprisesa display to present a graphical representation of the sensed valuedetermined based on the previous output measurement after identifyingthe artifact condition.